Photogrammetry engine for model construction

ABSTRACT

A system and methods for accessing 2D digital images and 3D geometric models over a network (preferably the Internet) and transforming and composing that media along with 2D digital image and 3D geometric model media, acquired by other means, into enhanced 2D image and 3D model representations for virtual reality visualization and simulation is disclosed. Digital images and models from a network and other sources are incorporated and manipulated through an interactive graphical user interface. A photogrammetric media processing engine automatically extracts virtual sensor (camera) and geometric models from imagery. Extracted information is used by a reconstruction processor to automatically and realistically compose images and models. A rendering system provides real-time visualization and simulation of the constructed media. A client-server processing model for deployment of the media processing engine system over a network is disclosed.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.09/344,814 filed on Jun. 25, 1999, now U.S. Pat. No. 6,912,293 whichclaims priority of U.S. Provisional Patent application Ser. No.60/090,749, filed Jun. 26, 1998 and incorporates that application hereinby reference for all purposes.

BACKGROUND OF THE INVENTION

A parametric solid modeler is an approach to creating geometric modelsof 3D objects and scenes. Constructive Solid Geometry (CSG) and BoundaryRepresentation (Brep) methods are two fundamental solid modelingmethods. CSG uses solid primitive shapes (cones, cylinders, torus,spheres, rectangular prisms, etc.) and boolean operations (unions,subtractions, intersections) to create a solid model. A cylindersubtracted from a cube produces a hole, for instance. Brep methods startwith one or more wireframe profiles, and create a solid model byextruding, sweeping, revolving or skinning these profiles. The booleanoperations can also be used on the profiles themselves and the solidsgenerated from these profiles. Solids are also created by combiningsurfaces through a sewing operation. Most commercial solid modelingsystems are hybrids combining both CSG and Brep methods to create thedesired models.

In a parametric model, each geometric entity has parameters associatedwith it. These parameters control the various geometric properties ofthe entities, such as the length, width and height of a rectangularprism, or the radius of a fillet. They also control the locations ofthese entities within the model. Parameters are changed by the user tocreate a desired model.

If the task is to construct a 3D model of a scene or object depicted ina sketch or a photograph, the use of a conventional solid modelingsystem can be an arduous task. This is especially true when producing atexture-mapped model, where the original photograph or other imagery isapplied to the derived geometric model. The current process typicallyinvolves first creating a 3D geometric model then tediously “cutting andpasting” textures onto the model to add realism. Ultimately, the resultsare limited by the hand-eye coordination of the user. In short, thecurrent approach and available tools cannot achieve desired levels ofgeometric accuracy, realism (visual fidelity), are too time-consuming,and require too much skill.

SUMMARY OF THE INVENTION

One application of the graphics system described herein is forvisualizing the placement of one or more physical objects in a physicalscene. In general, digital images of those one or more physical objectsand a digital image of the physical scene are input to the graphicssystem and the graphic system maps the objects in those images to athree-dimensional geometric model using cues in the digital image orprovided as additional inputs. The graphics system then generates animage output that is an image of the geometric model, thereby showing avirtual image of the physical objects in the physical scene withoutactually moving those physical objects into the physical scene.

One use of the graphics system is for electronic commerce. For example,a consumer captures a digital image of a room in the user's home andprovides it to the graphics system. A merchant captures digital imagesof products it has for sale. Thus, the graphics system can combine theimages to show the consumer an image of what the product might look likewithin the room where the user might place the products, if purchased.

If the user cannot capture all of the room in one image, the user cancapture multiple images with some overlap and the graphics system willmatch them up to form a “mosaic” image that can then be mapped to ageometric model.

In both the consumer and merchant images, the image provider mightinclude cues to assist the graphics system in mapping thetwo-dimensional object onto the three-dimensional model. For example,the user might have an object in the scene that is a rectangular prism(such as a box, dresser, refrigerator, etc) and indicate the bounds ofthe rectangular prism on the two-dimensional image. The graphics systemcan handle shapes besides rectangular prisms, such as spheres, flatrectangles, arches, boolean combinations of simple shapes, etc., limitedonly by the shapes represented in a shape library that forms part of thegraphics system. In a typical user interface, the user selects a shapewhile viewing an image and then moves the shape around to approximatelycoincide with an object in the image.

Because the images are mapped to a geometric model, the images of thescene and the objects to be inserted do not need to be taken from thesame camera position. For example, a consumer might capture an image ofa room from an angle, standing at the edge of the room, but a merchantmight have captured an image of a rug they are offering for sale withtheir camera directly over the rug. As part of the processing performedby the graphics system, it determines camera parameters, such as cameraposition in the geometric model, camera rotation, focal length, centerof view, etc. Those parameters might already be available if the imagecapturing device is sophisticated or the camera is always held in aknown position, but such parameters are usually not available and thegraphics system will generate them from the images and constraints.

The graphics system could be a localized system or a client-serversystem. For example, a server connected to clients over the Internetmight be used to create an electronic commerce system in connection withone or more Web sites. Merchants could provide catalogs of goods to theserver and the consumer could provide an image of a scene to the server.The server could then return to the consumer an image of how the productwould look in the scene. Since the images are mapped into athree-dimensional geometric model, the combination of the objects andthe scene appear more realistically combined, even when taken withdifferent camera parameters, than if the image of the object were justdistorted and overlaid into an area of the image of the scene.

In variations of the basic systems, complex interactive objects can beprovided for combination with a scene. For example, if the merchantprovides multiple images of an object, the consumer can place the objectin a scene at any angle. An object such as a chest of drawers mightinclude the ability to open a drawer and view the effect. Additional,non-image information, such as a product description, pricing, etc.,might also be included with a complex object.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a high-level block diagram of a media processing engineresiding on a network in a client-server configuration.

FIG. 2 is a detailed block diagram of the media processing engine withinputs and outputs to a network or otherwise.

FIG. 3( a) is a wedge structure; FIG. 3( b) is a box structure; FIG. 3(c) is another box structure.

FIG. 4( a) shows a cylinder structure; FIG. 4( b) shows a spherestructure; FIG. 4( c) shows a two box structures constrained to eachother by equating point elements; FIG. 4( d) shows a scene graph withtwo nodes and one link.

FIG. 5( a) shows examples of boolean union, merge, intersection, anddifference; FIG. 5( b) shows several three dimensional geometricstructures and associated geometric elements and two dimensionalfeatures corresponding to a projection of the 3D elements into 2D.

FIG. 6( a) is an image of an object; FIG. 6( b) is the image of theobject with annotations added to identify individual edge and pointfeatures of the object shown in the image.

FIG. 7 is the same image of the object with annotations added toidentify connected structure edge and point features of the object shownin the image.

FIG. 8( a) shows a 3D construction with the image of FIG. 6( a) with anunsolved box structure in its default initial position and an unsolvedcamera model; FIG. 8( b) shows a scene graph containing the boxstructure; FIG. 8( c) is the scene graph with a sphere structure added;FIG. 8( d) is the same scene graph with yet another structure added.

FIG. 9( a) is a 3D object construction containing two box structure andone sphere structure and corresponding to the scene graph of FIG. 8( d);FIG. 9( b) is the scene graph of FIG. 8( d) with an additional cylinderstructure added.

FIG. 10( a) shows the relationship between a line in 3-space, a camerasystem in the same space, and the projection of that line into theimaging plane of the camera; FIG. 10( b) shows the construction of anerror function quantifying the error between a line segment annotatedinto the imaging plane of the camera and the projection of the 3D linethrough the camera and into the imaging plane

FIG. 11( a) shows another view of the 3D construction of FIG. 8( a).FIG. 11( b) shows the back projection of the 3D box into the image forthe construction of FIG. 8( a) and unsolved parameters; FIG. 11( c)shows a view of the construction of FIG. 8( a) with camera and boxparameters recovered; FIG. 11( d) shows the back projection of the 3Dbox into the image for the construction of FIG. 8( a) and solved cameraand geometry parameters.

FIG. 12( a) shows a sphere structure constrained to the top of a boxstructure pertaining to the construction of FIG. 9( a); FIG. 12( b)shows the same construction with a boolean merge operation appliedbetween the box and sphere structures; FIG. 12( c) shows construction ofFIG. 12( b) with a box structure added; FIG. 12( d) shows theconstruction of FIG. 12( c) with a boolean difference operation appliedbetween the two box structures; FIG. 12( e) shows the construction ofFIG. 12( d) with a cylinder structure added; FIG. 12( f) shows theconstruction of FIG. 12( e) with a boolean merge applied between thecylinder structure and the second box structure.

FIG. 13( a) shows a rectangular straight pipe extruded solid from arectangle extrusion profile and linear extrusion path; FIG. 13( b) showsa curved profile extruded solid tube from a curved profile and curvedextrusion path; FIG. 13( c) shows a revolved solid generated from acurved profile and revolution axis; FIG. 13( d) shows a constructionemploying an extrusion; FIG. 13( e) shows a construction employing arevolution.

FIG. 14 is a flow diagram showing the process for constructing 3Dobjects as assemblages of structures.

FIG. 15 is a flow diagram showing visibility processing on geometricmodels to determine which subfacets of the model each associated camerasees.

FIG. 16 is a flow diagram showing how visibility between cameras andgeometry is resolved to reapply imagery back onto geometry.

FIG. 17( a) is a diagram of a Phantom Cursor graphical user interfacemechanism; FIG. 17( b) is a diagram of the default 3-space constructioncreated upon initialization of a phantom cursor.

FIG. 18( a) is an image of a room scene; FIG. 18( b) is the image of theroom scene with a default quadrilateral phantom cursor graphic in itsinitial “floating” position; FIG. 18( c) shows the image of the roomscene with the phantom cursor graphic modified by the user, FIG. 18( d)shows the phantom cursor image annotation overlay from FIG. 18( c); FIG.18( e) shows the 3-space construction for the current phantom cursorexample.

FIG. 19 is a flow diagram outlining the phantom cursor process.

FIG. 20 is a flow diagram outlining an image mosaic process leveragingthe phantom cursor process.

FIG. 21 shows three images of a room scene used as example input to thesystem mosaic processing method.

FIG. 22 shows the three images with annotations applied as part of themosaic processing.

FIG. 23 shows the 3-space construction corresponding to the mosaicprocessing of the images of FIG. 22.

FIG. 24( a) shows a detailed diagram of a quadrilateral phantom cursorstructure; FIG. 24( b) gives the parameterization of the vertices of aquadrilateral phantom cursor; FIG. 24( c) is a correspondence table thatmaps the relationship between 2D edge features and 3D elements of a quadphantom cursor.

FIG. 25 is an image created by mosaic of the three images of FIG. 21.

FIG. 26 is a flow diagram of an image collage process that leverages thephantom cursor process.

FIG. 27 is an image of a room scene with annotations added for collageprocessing.

FIG. 28( a) is an image of a rug product to be used for the collageprocessing; FIG. 28( b) is an image of a picture product to be used forthe collage processing.

FIG. 29( a) shows the 3-space construction corresponding to the collageprocessing of the image of FIG. 27; FIG. 29( b) is the scene graphcorresponding to the collage construction of FIG. 29( a).

FIG. 30 is an image that is the collage composition of the image of FIG.27 with the product images of FIG. 28( a) and FIG. 28( b).

FIG. 31 shows an image of a room scene and an image of a televisionobject to be composed with the scene image through collage processing.

FIG. 32 shows an image of the room scene after composition with theimage of the television, as well as a generated alpha image that willcorrect undesired hidden surface regions.

FIG. 33 shows a final image of the collage construction of FIG. 32 afterapplication of the alpha image.

FIG. 34 is a flow diagram outlining the process of creating a 3D scenemodel from one or more images and levering the phantom cursor.

FIG. 35 is an image of a room interior with annotations added forconstruction of a 3D model of the room based on the process outlined inFIG. 34.

FIG. 36 is a 3D model of the room depicted in FIG. 35.

FIG. 37 is a scene graph corresponding to the 3D model of FIG. 36.

FIG. 38( a) is an example of an intelligent object; FIG. 38( b) showsthe scene graph of an intelligent object depicted in FIG. 38( a).

FIG. 39 is a flow diagram showing how to integrate constructed objectand scene models with various interaction and scaling options.

FIG. 40( a) is an image of a room scene with an annotation added to thedepicted floor” representing the anchor point for an object to beinserted; FIG. 40( b) is the image with the rug object of FIG. 28( a)inserted at the anchor point on the floor.

FIG. 41( a) is the image with the rug object moved along the floor, awayfrom the initial from the original anchor point; FIG. 41( b) is the sameimage with the rug moved to some other location on the floor.

FIG. 42( a) shows the scene graph of the scene construction for thescene of image of FIG. 40( a) prior to insertion of the rug object; FIG.42( b) shows the construction scene graph after the insertion of the rugobject; FIG. 42( c) shows the constructed scene model with the insertedproduct model.

FIG. 43 shows the 3-space construction for a “force-fit” modelintegration procedure.

FIG. 44( a) is an image of a brown wooden CD cabinet; FIG. 44( b) is animage of a cubical pine storage box.

FIG. 45( a) is an image of a dark brown wood cabinet; FIG. 45( b) is animage of a cubical pine storage box.

FIG. 46 shows four images of a room scene generated by the constructionof a 3D model of the room in FIG. 35, insertion of constructed 3D modelsof the products depicted in FIG. 44 and FIG. 45, and user-interactivenavigation of the scene and movement of the product models within thescene.

FIG. 47 shows a 3D graphical product information display that “pops-up”provide information about a product.

FIG. 48 shows the room scene of FIG. 46 with both 2D and 3D productinformation displays active.

FIG. 49 is a diagram of multiple instances of the media processingengine operating over a network in client-server configuration.

FIG. 50 is a diagram of multiple instances of the media processingengine operating over a network in client-server configuration andoperating as an electronic commerce merchandising system.

DESCRIPTION OF THE SPECIFIC EMBODIMENTS

A photogrammetric processing engine placed between the user and thesolid modeling engine transforms the conventional “hand-eye” coordinatedmodeling interface to one driven by user-interaction with the inputphotographs. Photogrammetry is the art, science, and technology ofobtaining reliable information about physical objects and theenvironment through processes of recording, measuring, and interpretingphotographic images and patterns of electromagnetic radiant energy andother phenomena.

The user paradigm becomes one of molding and sculpting directly fromimagery. Unlike conventional solid modeling systems, which require theuser to manually size and place modeling geometry, the systemautomatically sizes and positions (parameterizes) geometric entities,thus “fitting” 3D model entities to with 2D image features.

The system also automatically determines 3D parametric camera models foreach photograph of the scene (e.g. location, pose, focal length,center-of-projection). Having derived geometry and camera models, thesystem automatically produces a visual reconstruction and rendering ofthe scene through re-projection of the input imagery onto derivedgeometry.

Many types of planar and volumetric 2D image and 3D model constructions,compositions, and visualizations are realized. Methods for 2D imagemosaics and collages, 3D object and scene constructions, andcompositions of these are disclosed. With the underlying image-baseduser paradigm, these methods are highly intuitive and enable a noviceaudience.

A network-based client-server system leveraging the solid modelingsystem is disclosed. This system is suited for a wide range ofapplications, from professional CAD development and collaboration toInternet e-commerce activities.

The Internet is rapidly becoming a dominant medium for commerce. Onearea enjoying rapid growth is that of online shopping. While theInternet offers unprecedented convenience to consumers searching andresearching products, there exist barriers to consumer willingness orability to purchase certain products online. High among these barriersis a shopper's desire to physically see or try out a product ahead oftime.

To reduce such barriers, great effort is underway to deliver richerdigital content to online product catalogs. At present, one solution isthe creation and addition of interactive photorealistic 3D productmodels. The basic idea is to bring “virtual showroom” experiences toonline shopping.

While this capability is useful for products such as watches and tennisshoes, it is limited for items that need to be evaluated within thecustomer's application context. In shopping for kitchen cabinets, aconsumer is most interested in how they would look in their home, not ona web page.

Disclosed is an e-commerce system allows online shoppers to “take home”products from online catalogs and try them out as an integral part oftheir online shopping experience. In one embodiment, as a client-serversystem, a merchant serves a client application enabling end-users toconstruct 3D representations of their environments from their owndigital photography. With a target scene constructed, a server nodeserves functional models of products that are readily composed with theend-user environments and allow the end-user to understand how theylook, fit, and function.

Media Processing Engine (MPE)

Photogrammetric Media Processing Engine (MPE) 11 is shown in FIG. 1. Amore detailed diagram is shown as FIG. 2. The main functional componentsof MPE 11 are Graphical User Interface (GUI) 12, PhotogrammetricModeling Engine (PE) 13, Scene Graph and Ancillary Data Structures (SG)14, Built-in 3D Parametric Object and Scene Geometry Library (BL) 18,Solids and Surfaces Geometry Engine (GE) 15, Visual ReconstructionEngine (VE) 16, and Rendering Engine (RE) 17.

Digital data and program transmission network 10 is a source of data andprogram input and output of the system and is an integral component andbackbone of the client-server data processing model. In its currentembodiment, the network is the Internet. However, the system is notlimited to any particular physical data transmission network and datatransmission protocol.

All data input, output, and process control is directed byuser-interactive input 25 through Graphical User Interface 12. Thedisclosed invention system and methods are not dependent on a particulardesign and implementation of 12.

The system imports and exports 2D digital images as a fundamental datatype. User digital images 24 are those acquired or obtained by the userand supplied to the system or the network. Examples of user images arethose acquired from acquisition devices such as a digital cameras andscanners or transferred from mediums such as CDROM. User digital images24 are imported into the system or uploaded to network 10. Networkdigital images 21 are digital images downloaded from network 10. Userdigital images might also be downloaded as network images 21. As anexample, user images are processed by a third and delivered over theInternet.

The system imports and exports 3D geometric models as a fundamental datatype. Geometric models are typically imported and exported with sets of2D image texture maps assigned to the models. User 3D geometric models23 are those acquired or obtained by the user and supplied to the systemor the network. Examples of such models are those acquired fromacquisition devices such as scanner devices or transferred from mediumssuch as CDROM. User 3D models are imported into the system or uploadedto network 10. Network 3D models 20 are models downloaded from network10.

Ancillary data input 22 imports general and information from network 10.For example, for images 21 and models 20, ancillary data 22 mightspecify size, color, price, and availability parameters. The user mayalso supply ancillary data, such as certain known dimensions of anobject or feature, through user interactive input 25.

The system imports and exports project databases providing a convenientmechanism for project saving, restoration, and work collaboration.Project databases include system generated scene graph and other datastructures and accompanying data. Data 22 imports project databases andall general data associated with incoming 2D and 3D media from network10. Data input 28 imports project databases from storage medium such assystem memory or disk. Output 26 exports project databases to storagemediums such as system memory or disk. Output 27 exports projectdatabases to network 10.

System output 26 consists of 2D images, generated 2D images and imagecompositions, 3D models, 3D models and 3D texture-mapped object andscene compositions generated from 2D images, and compositions of all ofthese. Output 26 is directly viewed on a computer display device, storedon digital storage mediums such as computer hard disk. System output 27sends system output 26 to network 10.

Scene graphs are used to store geometric, topological, and visualconstruction information. A scene graph conveniently and efficientlyencodes a hierarchical organization of the components that comprise thescene. The scene graph and ancillary data structures are stored in datastructures unit 14.

A scene graph is made up of nodes that represent geometric componentsdrawn, properties of the components, hierarchical groupings of nodes,and other information such as cameras and visual reconstructioninformation. Nodes are connected by links in a parent-child hierarchy.The scene graph is processed or traversed from node to node by followinga prescribed path of links. Nodes and links contain parameters. Nodescontain the parameters associated with the geometric structures theyspecify. Links contain the spatial relationships (e.g. rotation,translation, scale) between the geometric primitives (structures) of theinterconnected nodes. A tree structure is an example of one of the mostcommon types of graph structures. Another form of scene graph is adirected acyclic graph (DAG). The specific form of the scene graph isnot essential to the invention as described here. FIG. 4( d) shows ascene graph 55 with two nodes and one link.

For media construction, photogrammetric modeling engine 13 and visualreconstruction engine 16 read from and write to the scene graph datastructure. For media presentation, rendering engine 17 reads the scenegraph data structure and composes a visual representation.

The Photogrammetric Modeling Engine 13 is highly-automatedphotogrammetry and constraints based modeling system that embodies aprocess for recovering parametric 3D geometric structure and cameramodels from 2D images. Under the direction of user interactive input 25,the system produces parametric and non-parametric polyhedral and solid(volumetric) models of image compositions and 3D models of objects andscenes depicted in supplied 2D digital imagery. Modeling Engine 13recovers parametric camera models for each input image of a constructionproject.

Geometric constructions generated by the system are hierarchical andnon-hierarchical assemblages of geometric components called“structures”. Structures are parameterized geometric primitives, such aspoints, lines, line segments, planes, cubes, squares, rectangles, boxes,cylinders, cones, frustums, wedges, surfaces of revolution, andextrusions.

A structure contains a set of parameters describing its size and shape.The coordinates of each vertex of a structure are expressed as a linearcombination of these parameters, relative to the structure's internalcoordinate frame. The spatial extent of a structure is specified as abounding volume. In FIG. 3( a), half-wedge structure 49 is defined byshape parameters wed_width, wed_height, and wed_depth. A vertex VE1 isexpressed as (−; wed_width, wed_depth, 0). The spatial extent ofstructure 49 is defined by rectangular bounding volume 54. In FIG. 4(a), cylinder structure 50 is described by three parameters cyl-radiusA,cyl-radiusA, and cyl_length. The first two parameters control diameterand the third controls length. When cyl-radiusA, and cyl-radiusA areequal, the cylinder is circular; otherwise the cylinder is elliptical.The extent of the cylinder is given by bounding box 58. The cylindermight also contain radius parameters at the “other end” of the cylinder,thus defining tapered cylinder profiles. In FIG. 4( b), sphere structure51 is described by radius parameter sphere-radius and bounding box 59.

Structures are also generated, as in the case of extruded and revolvedsolids. Shown in FIG. 13( a) is an extruded rectangular box structureXX. It is generated by translating rectangular profile element X10 alonglinear extrusion path element P10. FIG. 13( b) shows an extruded objectXX with a more complex curved profile element X20 and curved pathelement P20. FIG. 13( c) shows a solid structure XX generated byrevolving profile element X30 around sweep axis element P30.

The system embeds a library of built-in structures 18, including, butnot limited to, fundamental geometric structures typically associatedwith conventional solid modeling systems. Library 18 is fully extensibleand the number and types of structures supported may be tailored to suitthe application software used. New classes of structures arepre-programmed into built-in library 18, imported as user input 23, ordownloaded as network input 20.

Geometric structures are comprised of elements. An element is acomponent of a structure or higher-level construct or possibly a 3Dstructure itself, as in the case of a point. It represents a 3Dstructure part or shape that can be transformed and projected into theplanes of related 2D input images. Examples of elements include points,lines, line segments, planar polygons, circles, and curves.

In FIG. 3, structures 49, 52, and 53 comprise edge and point typeelements. Point elements are structure vertices and edge elements areline segments forming edges between structure vertices. In FIG. 3( b),“box” structure 52 has eight vertex elements, VE40 through VE47.Connecting these vertex elements are 12 edge elements. For example, edgeelement EE24 is the line segment formed between vertices VE42 and VE46.

The number and type of elements supported by modeling engine 13 is fullyextensible. The system does not limit the vocabulary of geometricelements supported. Any component of a geometric structure orhigher-level construct that can be projected and identified in theimaging plane of a camera (plane of an image) may be defined as anelement. FIG. 5( b) shows several examples of 3D structures andcomponent element types. Point 64 is itself a structure and an element.Edge structure 66 is and edge element comprised of two vertex (point)elements and a connecting line segment. Box structure 68 containsindividual point elements and edge elements. Cylinder structures 70 and74, and sphere structure 72 are comprised of point elements, edgesegment elements, circle, and ellipse elements. For generatedstructures, such as the extruded and revolved solids of FIG. 13,elements include defining profiles and paths.

The relationships between component structures in the hierarchy of amodel assembly are represented by parametric operators, which encodespatial and geometric relationships between structures. In the presentembodiment, spatial operators are the affine geometric transformationsof rotation R, translation T, and scale S. In general, operators mayalso specify non-affine spatial transformations. Geometric operatorsdetermine the topology of the overall model assembly. FIG. 5( a) showsthe boolean operators union 60, merge 61, intersection 62, anddifference 63. Geometric operators also include, but are not limited to,blending, sweeping, imprinting, covering, lofting, skinning, offsetting,slicing, stitching, sectioning, and fitting. Geometric operators areprocessed by geometry engine 15.

3D geometric models are related to 2D images through annotations markedon images called features. A feature represents the projection of a 3Dmodel element into the plane of projection of the camera attached to theimage. The parameterized camera model attached to the image specifiesthe projective transformation.

FIG. 5( b) illustrates several projective relationships between 3Dstructure elements and 2D image features. Point, line segment, andcircle elements in 3-space project into point, line segment, and ellipsefeatures in 2-space, respectively. 3D point structure 64 projects into2D point feature 65. 3D edge structure 66 projects into 2D edge feature67. 3D point and edge segment elements of box structure 68 and cylinderstructures 70 and 74 project into point and edge features 69. 3D circleelements of cylinder 70 and ellipsoid elements of cylinder 74 projectinto 2D ellipse features 71. 3D circle features of sphere 72 projectinto 2D circle features 73.

The modeling engine modeling process entails the marking of features inone or more input images and the establishment of correspondence betweenthese 2D features and geometric elements of a 3D construction. In thepresent embodiment, the user places features in images throughuser-interactive input 25 and interface 12. An optional gradient-basedtechnique aligns marked edges to image pixel features with sub-pixelaccuracy.

As an example, image I100 of FIG. 6 is annotated with edge and pointfeatures, resulting in image I101. In image I101, edge features F20through F31 mark selected edge regions. Geometrically, each 2D edgefeature is composed of two endpoint vertices and a connecting linesegment. For example, edge feature EF24 contains endpoint featurevertices VF10 and VF11. The endpoints of marked edge features need notalign with depicted endpoints of a selected edge in the image; theplaced line segment need only align with the associated edge in theimage. In image I101, in addition to edge features, point features VF50and VF60 mark individual point feature positions in the image.

Correspondence establishes a relationship between 3D elements ofstructures in global position and 2D image features. A singlecorrespondence is a feature-element pair. A geometric element can havemultiple image correspondences.

Correspondence types define valid feature-element pairs that quantifythe degree of geometrical coincidence of the projected view of ageometric element and a 2D image feature, relative to the cameraassociated with the image. Valid correspondence types are defined foreach geometric element or higher-level construct. Examplefeature-element types include point-point, point-edge, and edge-pointpairs. The system is fully extensible and not limited to a specificvocabulary of correspondence types.

The degree to which an annotated 2D image feature and a projectedgeometric element align is expressed in terms of a correspondence error.The manner by which the correspondence error is measured depends on thecorrespondence type. A correspondence error metric is defined for eachvalid correspondence type and quantifies the degree of geometricalcoincidence of the projected view of the element and the feature.

Correspondences are established implicitly or explicitly. In the formercase, features are placed in images and 3D structure elements areexplicitly associated with them. In the latter case, images areannotated with features that directly (implicitly) correspond tostructures and their elements. Correspondences may also be implicitlyestablished by application program design.

As an example of explicit correspondence, edge correspondences areestablished between “box” structure 52 of FIG. 3( b) and image I100 ofFIG. 6.

Through user-interactive input 25 and interface 12, individual 2D edgefeatures EF20, EF21, EF22, EF23, and EF24 are annotated, as shown inimage I101. These features are then explicitly assigned to 3D structure52 edge elements EE20, EE21, EE22, EE23, and EE24, respectively. In thepresent implementation, feature-to-element pairings are establishedthrough a “point-and-click” user interactive interface.

With implicit assignment, structures are associated with images directlyas feature annotations. For the present example, image I101 is annotatedwith structure 52 by placing 2D vertex features into the image thatdirectly correspond to 3D vertex elements VE40 through VE47, as shown inFIG. 7 image I103. In doing so, edge elements adjoining the verticesimplicitly correspond to (and serve as) edge features in the image. Inthe present implementation, as structure vertices are placed,interconnecting edges “rubber-band” in place, thus presenting thestructure as a “pliable” 2D annotation object.

Constraints are values set for parameters of a construction. By reducingthe number of unknown parameters of a construction, the computationalload of parameters to be recovered by modeling unit 13 is reduced.Constraints may be placed on any values of a constructionparameter-space, including structure shape parameters, spatial andgeometric structure operators, and intrinsic and extrinsic cameraparameters. Constraints on the parameter-space of a 3D model and cameraconstruction may be defined by the application or explicitly specifiedby user-interactive input 25 through 12.

Structures may have their individual shape parameters constrained tospecific fixed values. For example, setting structure 52 parametersbase_width, base_depth, and base_height to x units each establishes acube structure of 2× units in each dimension. In the present embodiment,the system treats parameters symbolically, so parameters areconveniently shared between structures. Equality is a common type ofconstraint. For example, if FIG. 3 structure 53 parameter door_depth isset equal to structure 52 parameter base_depth, the depth of bothstructures remain the same, even under varying values of base_depth.

Constraints on spatial operator parameters fix the placement ofstructures. In FIG. 9( a), structures 51, 52 and 53 are spatiallyconstrained by the rotation R, translation T, and scale S operatorslinking them. In this example, with the given default bounding volumes,R is set to null (no rotation in any axis) between 51 and 52 and between52 and 53. Between 52 and 53, translation T is set such that themidpoints of structures 52 and 53 align in the x-axis and z-axis and theminimum bounding extents of structures 52 and 53 are coincident inthey-axis. Between structures 51 and 52, translation T is set such thatthe midpoints of structures 52 and 53 align in the x-axis and z-axis.Scale S is set to unity for all structures.

As another example, a user interactively selects and equates structurevertex elements. In FIG. 4( c), structure 53 is attached to the top ofstructure 52 by the user selecting and equating vertex elements VE44 andVE13, VE47 and VE16, and VE46 and VE15. This constraint specificationsets the spatial translation T and rotation R between the structures tozero while also equating their width and depth shape parameters.

Cameras

Associated with each input image is a 3-space camera system that modelsthe imaging process between structures in 3-space and correspondingfeatures in 2-space. Camera models are parametric and include variableparameters describing external pose and internal settings. For pose,three parameters Cx, Cy, and Cz describe 3-space position and threeparameters Rx, Ry, and Rz describe angular viewing direction. Forinternal calibration, parameters include focal length f andcenter-of-projection parameters cop_x and cop_y. A camera's pose andinternal projection parameterization is composed into a homogenous 4×4matrix. The composition of the intrinsic projection transformation andextrinsic pose transformations specify the mapping of an arbitrary 3Dpoint in the global 3D coordinate frame into the camera's image plane.

Geometry and Camera Reconstruction Processing

In contrast to a conventional modeling system which requires the user tomanually size and place modeling geometry, the modeling engine 13automatically sizes and positions (parameterizes) geometric structures.Modeling engine 13 also determines 3D camera models (location, pose,focal length, center-of-projection). Camera model derivation is anintimate component of the photogrammetric process and is central tosubsequent visual scene and object reconstruction.

Modeling engine 13 recovers all unknown unconstrained variables of aconstruction parameter space to form a reconstruction of depictedobjects or scenes and the cameras that image them. The solution of thegeometry and camera parameterization aligns 3D model elements withcorresponding 2D image features and allows recovered camera system toaccurately project (re-project) their imagery onto geometricrepresentations.

Modeling engine 13 solves for the variable parameters by minimizing theaggregate correspondence error (sum of all correspondence errors). Thisis a nonlinear multivariate function referred to as the objectivefunction O. Minimizing the objective function requires nonlinearminimization. Defined constraints eliminate variables and thereby reducethe dimensionality of the unconstrained nonlinear minimization process.In addition, it makes use of invariants and linearities to progressivelyminimize the function.

FIG. 10( a) illustrates an edge-to-edge correspondence type andimplementation of the present embodiment. Line element E70 of a 3D modelprojects onto image plane 71 of camera C10. The 3-space line E70 isdefined by a pair of vectors (v,d) where v represents the direction ofthe line and d represents a point on the line.

The world coordinates P_(w)(X) of a structure vertex P_(s)(X) isP_(S)(X)=s₁(X) . . . s_(n)(X)P_(S)(X), where X represents the vector ofall structure parameters and S₁(x) . . . S_(n)(X) represent the spatialoperators linking structures. The world orientation v_(w)(X) of aparticular line segment v(X) is given as v_(w)(X)=S₁(X) . . .S_(n)(X)v(X).

The position of camera C10 with respect to world coordinates isexpressed in terms of a rotation matrix Rj and a translation vector Tj.The normal vector m is computed as m=Rj(v×(d−; Tj)).

The projection of line element E70 onto image plane 71 is projected edgesegment B74. Edge segment B74 is the intersection of plane 73 defined bym with image plane 71, located at z=−f where f is the focal length ofthe camera. The computed image edge segment B74 is defined by theequation m_(x)x+m_(y)y−; m,f=0.

An annotated image edge feature F75 is delimited by image featurevertices VF20 and VF21, with image coordinates (x₁, y₁) and (x₂, y₂)respectively, and is denoted as {(x₁, y₁),(x₂, y₂)}.

The disparity (correspondence error) between a computed image edge B74and an annotated image edge F75 is Err_(I), for the ith correspondenceof the model construction. FIG. 10( b) shows how the error between edgesB74 and F75 is calculated. Points on the annotated edge segment F75 areparameterized by a single scalar variable sε[0,1] where l is the lengthof the edge. A function, h(s), returns the shortest distance from apoint on the segment, P(s), to computed edge segment 74.

With these definitions, the total error between marked edge segment F75and computed edge segment B74 is calculated as

${Err}_{1} = {{\int_{0}^{l}{{h^{2}(s)}\ {\mathbb{d}s}}} = {{\frac{1}{3}\left( {h_{1}^{2}{{h_{1}h_{2}}}h_{2}^{2}} \right)} = {{m^{T}\left( {A^{T}B\; A} \right)}m}}}$m = (m_(x), m_(y), m_(z))^(T) $A = \begin{pmatrix}x_{1} & y_{1} & 1 \\X_{2} & y_{2} & 1\end{pmatrix}$$B = {\frac{l}{3\left( {m_{x}^{2} + m_{y}^{2}} \right)}\begin{pmatrix}1 & 0.5 \\0.5 & 1\end{pmatrix}}$

The reconstruction minimizes the objective function O that sums thedisparity between the projected edge elements of the model and themarked edge features in the images. The objective function O=ΣErr_(i) isthe sum of the error terms resulting from each correspondence i. O isminimized using a variant of the Newton-Raphson method, which involvescalculating the gradient and Hessian of O with respect to the variableparameters of the camera and the model.

In the present embodiment, symbolic expressions for m are constructed interms of the unknown model parameters. The minimization differentiatesthese expressions symbolically to evaluate the gradient and Hessianafter each iteration. The reconstruction algorithm optimizes over theparameters of the model and the camera positions to make the modelconform to the observed edges in the images.

Modeling engine 13 writes to, and reads from, scene graph unit 14. Thisdata exchange includes 2D images 30, paramaterized geometric structures31, parameterized operators and constraints 32, parameterized cameramodels 33, and ancillary data 34 which includes images features andcorrespondences.

Solids and Surfaces Geometry Engine (GE) 15 reads the scene graph andperforms specified geometric operations on the construction. Theresulting fundamental and complex geometric structures and models aresent back to scene graph unit 14 via 39 or are stored in built-inlibrary 18 via 35 and augment the library. The latter generatesstructures beyond those pre-existing in 18. The output 40 of geometryengine 15 is also sent to visual reconstruction processor 16.

GE 15 supplies operations on and between all structure classes. Thisincludes solid structures operating on surfaces structures and viceversa. Operations on and between geometric structures include, but arenot limited to, booleans, extrusions, sweeps, revolutions, lofts,blending, shelling, and local manipulation and deformation. For example,boolean operations between a given set of shapes allow the creation ofan infinite number of simple and complex shapes.

FIG. 5 shows example boolean operations union 60, merge 61, intersection62, and difference 63 performed by GE 15. Geometric operators alsoinclude, but are not limited to, blending, sweeping, imprinting,covering, lofting, skinning, offsetting, slicing, stitching, sectioning,and fitting. FIG. 13( a) and FIG. 13( b) show examples of extrusionprocessing by GE 15. FIG. 13( c) shows an example ofsurface-of-revolution processing by GE 15.

The Visual Reconstruction Engine 16 automatically applies 2D inputimagery onto 3D geometric models using recovered 3D camera models. Theapplication of the input imagery onto the output geometry involves are-sampling of the input imagery, a process commonly calledtexture-mapping. Conventional texture-mapping systems typically requireusers to manually and explicitly specify texture-to-geometry mappings.Such conventional approaches are labor-intensive and yield inaccurateresults. Visual reconstruction engine 16 automatically determines thetexture mapping strategy for visually reconstructing scenes and objectson a facet-by-facet basis. This includes determination of which texturemap(s) to apply to each facet of the scene geometry, calculation of thetexture coordinate parameterization, and texture rectification (asrequired). Camera-image assignments are made to constructed geometrybased on the results of visibility tests and established image qualitycriteria.

The input to 16 is a database containing 3D object and scene geometrymodels, camera models 36, imagery 37 associated with each camera model,and geometric object and scene models 38 and 40.

Output 42 of 16 is a graphical database that is ready for processing byrendering engine 17 or export for further processing by anothergraphical database rendering apparatus or storage. The invention is notlimited to any particular geometry, camera, or texture data format orrendering procedure. The specific output of reconstruction engine 16output 42 is a function of the processing modality of media processingengine 11 and the media type produced.

For processing of 3D texture-mapped geometric object and scenes from oneor more 2D images, visual reconstruction processor 16 produces agraphical database that includes camera maps, texture maps andcoordinates, camera models, and 3D object and scene geometry. A cameramap is an assignment of cameras to scene geometry. For each triangularfacet of model geometry seen by at least one camera, a camera mapproduced by 16 designates a single “best” camera or group of cameraswhose imagery is available and appropriate to texture map the triangle.

Hybrid Camera-Geometry Visibility Processing

Visibility processing is required to determine, for each (camera, image)pair in a given 3D scene, which facets of scene geometry (in whole or inpart), are visible from the viewpoint of the camera. For visibleihiddensurface determination, the system considers back-facing surfaces,surface visibility with respect to the extent of each camera's viewingfrustum, and surface visibility within a camera's view frustum and inconsideration of occlusions (partial and full) amongst the varioussurfaces. The latter two categories are referred to here as windowvisibility and occlusion visibility, respectively.

Window visibility is resolved by intersecting surfaces with a camera'sviewing frustum, subdividing surfaces based on intersections, andselecting portions contained within the viewing frustum. Occlusionvisibility is resolved by computing the intersections of object surfaceswith each other. Then for each set of intersections, it is determinedwhich surface is closer to the viewer (camera) and thus visible.

Visual reconstruction engine 16 presents a hybrid visibility processingapproach, invoking both object-space and image-space visibilitycomputation. Image-space and object-space visibility processingalgorithms differ in the precision with which they compute the visiblesurfaces. Image space algorithms determine visible surfaces by examiningsurfaces at the pixel level, post-projection in to the imaging plane ofthe viewer. Object space algorithms directly compare surfaces in thedefined space. Object space calculations are employed for computing theintersections of surfaces in the defined global 3D coordinate system ofthe geometry. Image-space computations process geometry visibility inthe projected screen space of each camera. A hybrid approach ispreferred, since it can be fully implemented in software or make use ofcommonly available computer graphics hardware. It allows the system torun on a host computer systems with or without 3D graphics capabilities(software or hardware). For systems with 3D graphics capabilities,including hardware acceleration, the application can effectivelyleverage available resources.

Preferably, all geometry is processed as triangles, but any n-sidedpolygons or other representations could be used.

The visual reconstruction process is executed in two stages. The firststage of the visual reconstruction process is a determination of thevisible triangles relative to each scene camera. This is a multiple stepprocess carried out for each camera. FIG. 15 shows a flow diagram of theprocess.

At step ST70, all triangles are initialized as being visible and surfacenormals are computed for each. Triangles are backface filtered by takingthe dot product between the triangle normal n and the camera viewingdirection v determines if the triangle is back or front facing to thecamera. If back-facing, a triangle is marked as not visible.

At step ST71, each triangle not filtered out at step ST70 is clippedagainst the camera frustums. An object-space clipping algorithm checkseach triangle in three stages: trivial accept, trivial reject, and clip.If a triangle is trivially accepted, then it remains marked visible. Ifa triangle is trivially rejected (culled out of the cameras view), thenit is marked not visible to the camera. Otherwise, the triangle isintersected (clipped) against the camera frustum. The triangle is thentriangulated (subdivided) with the new intersection vertices. In thepresent implementation, Delaunay triangulation is employed. Theresulting sub-triangles that are within the view frustum are marked asvisible to the camera; those outside the frustum are marked as notvisible.

At step ST72, occluded triangles (surfaces) are identified. An exampleof an occluded triangle is one that is fully or partially obscured froma camera's viewpoint by another triangle or group of triangles.Triangles that are fully occluded are marked as not visible. Trianglesthat are not occluded at all remain marked visible. Otherwise, for eachpartially occluded triangle, a list of those triangles that occlude itis produced. At step ST72, the system or the user selects image-spacecomputation or object-space computation for resolution of hiddensurfaces. If an object-space computation is selected, process flowproceeds to step ST73. Otherwise, process flow proceeds to step ST74.

At step ST73, an object-space algorithm computes analytic visibility intwo stages. The first stage is a sort of the triangle database in3-space. The second stage is a determination of the visible trianglefragments in a camera imaging screen.

3D Scene Visibility Tree

The system utilizes a 3D binary space partition (BSP) tree to accomplisha global visibility sort of the input database. The BSP tree sortrecursively subdivides the object space and geometry with hyper-planesdefined by the surface facets of the input From any given arbitraryviewpoint, a subsequent traversal of the tree will deliver triangles ina spatially correct “back-to-front” or “front-to-back” ordering.

The scene visibility tree is traversed in a front-to-back order. Eachtriangle encountered is ray-projected into the camera imaging plane andinserted in a 2D BSP referred to as 2D camera visibility map.

2D Camera Visibility Map

A camera visibility map depicts the visible geometry of the scene, asviewed by a camera and projected into its imaging plane (screen). In theevent of inter-object occlusions, the visibility map resolves hiddensurfaces, depicting resulting visible geometry fragments. A cameravisibility map is constructed for each camera in the scene. Theobject-space camera visibility map is encoded as a 2D BSP tree. Intwo-dimensions, a BSP tree partitions a camera screen. Edges of theinput geometry projected onto the imaging plane define the linespartitioning the space.

The screen of the camera is partitioned into regions that are occupiedwith projected geometry (G-regions), and those unoccupied (U-regions).When a polygon (triangle) is inserted, it is and clipped to visitedU-regions. In the process, the clipped visible region of the polygonoverlapping the U-region becomes a G-region. That is, it is removed fromthe visible region of the screen.

For a triangle, 3 edges become 3 line partitions. Each line recursivelysubdivides the camera screen into two new sub-planes.

G-regions and U-regions appear as leaf nodes in the tree. G-regions aretagged to identify their specific origin. Insertion of geometry stopswhen there are no U-region nodes (screen is full). Intersections ofinput geometry and previously inserted geometry (G-regions) detected.This determines, for a given triangle, which, if any other trianglesobscure it. Fully occluded geometry is identified and tagged.

At step ST74, an image-space occluded surface algorithm is processed.The image-space algorithm may be implemented entirely in software orutilize 3D graphics hardware if available. The algorithm computesvisibility by rasterizing triangles into a 3D graphics frame buffer andalso has a z-buffer. Each triangle is rendered with a uniqueidentification value. To determine which triangles are fully occluded,all triangles are rendered into the frame buffer with z-buffer toresolve hidden surfaces. The frame buffer is subsequently scanned andthe triangle identification numbers present in the buffer are recorded.Those triangles whose identification numbers do not appear in the framebuffer are deemed fully occluded.

The determination of whether a given triangle is occluded, and if so, bywhich triangles, is as follows. For each triangle, a mask is generatedsuch that a scan of the previously rendered frame buffer will read onlypixels contained within the given triangle. The frame buffer is scannedand the triangle is determined to be fully visible if the pixel valuesread back are only equal to the triangle ID value. If this is not thecase, then the triangle is partially obscured, and the triangles whichobscure it are identified by the ID values found. Projected areas oftriangles (in pixels) are also determined by rendering a triangle andcounting the occurrences of its ID value in the frame buffer.

At step ST75, each occluded triangle is intersected (clipped) with allthose that occlude it. The triangle and intersections are Delaunaytriangulated. Fully occluded sub-triangles are marked as not visible.Others are marked visible. The projective area of each visiblesub-triangle is calculated.

The second stage of the visual reconstruction process is an assignmentof a cameras to triangles for texture mapping. FIG. 16 illustrates thisprocess.

For each triangle and for each camera which “sees” the triangle, theangle between triangle normal and camera view direction is calculated.In the present embodiment, the camera presenting minimum view angle andmaximum projective area is selected to map onto a given triangle.

Rendering Engine

Rendering Engine 17 is the visual rendering processor of mediaprocessing engine 11, producing all real-time and non-real-time visualoutput during media creation or playback. During interactive modelingand reconstruction processing with 11, 17 serves “in the loop” as therendering engine for all graphical display. Rendering Engine 17 readsscene graph and ancillary data unit 14 and constructs visual outputrepresentations. Visual output 26 is suitable for direct display on acomputer graphics display system or storage on a medium such as acomputer hard disk. Rendering Engine 17 output 27 is sent to network 10.

Output 26 of 17 includes 2D images, 2D Image mosaic compositions, 2Dimage collage compositions, 3D texture-mapped object and scenecompositions, and compositions of all of the above. The specific outputdepends on the processing modality of the system and the type of contentproduced.

The current and preferred embodiment of 17 is real-timeprojective-texture based apparatus and method for rendering a projectscene graph on a host computer that supports projective texture mappingin a 3D graphics API such as OpenGL or Direct3D. In general, a real-timecapability and use of these graphics API's are not required. In itspresent embodiment, 17 utilizes projective texture mapping [2] totexture map 3D geometric constructions directly from the original inputimages used to model the scene and the camera models recovered by thesystem. The rendering paradigm is one of treating the cameras in thescene as projectors, loaded with their images, and projecting theseimages back onto the recovered 3D geometric models. Since the 3D scenegeometry and camera models where recovered directly from these images,such a projection (re-projection) process completes the loop inreconstructing the scene. The invention also does not require thatrendering be executed using the projective texture mapping paradigm.Reconstruction processor 16 subsystem optionally outputs ortho-rectifiedtexture maps and non-projective texture coordinates for rendering usingnon-affine (orthographic) texture mapping.

For efficient texture map management and overall rendering, therendering engine 17 implements a tiling scheme for loading only thoseregions of interest of the input images required at any given stage ofthe rendering.

3D Object Construction

A process of constructing a parametric 3D solid object model from one ormore 2D images depicting the object is disclosed. In the presentexample, object constructions are compositions of volumetric structurecomponents. In general, modeling engine (PE) 13 and geometry engine (GE)15 support an extensive and extensible library of structure components,ranging from individual point structures to complex parametric surfacestructures. A 3D construction process flow diagram is shown as FIG. 14.

The process is exemplified with the construction of the domed-archobject depicted in image I100 of FIG. 6. The arch object is constructedwith structure components 50 and 51 of FIG. 4 and structure components52 and 53 of FIG. 3. The resulting model construction 100 is shown asFIG. 12( f). Scene graph 375 of FIG. 9( b) depicts the constructionhierarchy, with nodes N20, N21, N22, and N23 containing structures 52,53, 51, and 50, respectively.

At step ST50 the user imports an object image 24 or downloads an objectimage 21. Ancillary data 22, such as accompanying parametric descriptorsof objects depicted in imagery, is also downloaded at step ST50.Ancillary data might also be imported as data 24, for example in thecase of digital acquisition devices that provide such information.

In the present example object image I100 of FIG. 6 is downloaded fromnetwork 10. A default 3D construction coordinate system is establishedwith camera C100 belonging to image I100 assuming an arbitrary default3-space pose and internal parameterization.

At step ST51 a model structure is selected and instanced. In the presentexample, the user selects a “box” structure from built-in library 18which is instanced by the system as structure 52. Structure 52 serves asthe base of the domed-arch object construction and contains three shapeparameters base_width, base_height, and base_depth. By default, thefirst structure is spatially constrained to the origin of the 3Dconstruction coordinate system. FIG. 8( a) shows the initial sceneconstruction with structure 52 constrained to the origin of the 3-spacecoordinate system. The values of structure 52 shape parameters andcamera C100 internal and external parameters are initialized toarbitrary default values. The system inserts “base” structure 52 as rootnode N20 in scene graph 375 of FIG. 8( b).

At step ST52, through user-interactive input 25 and interface 12, theuser interactively places feature annotations in the current input imageand establishes correspondence between model structure elements andimage features. In the present example, line segment edge features EF20,EF21, EF22, EF23, EF24 are placed in image I100. These annotatedfeatures are shown in image I101 of FIG. 6. This set of features issufficient to identify the x, y, and z extents of the base object.Feature pairs EF20-EF21, EF22-EF23, and EF21-EF24 set x, y, and zdimensional extents, respectively. Annotated features EF22 through EF24are corresponded by the user to geometry edge elements EE20 throughEE24, respectively. In the present example, this correspondence is doneexplicitly by the user through a simple point-and-click mechanism within12.

Alternately, the establishment of feature annotations and theircorrespondence to structure geometry is done directly and implicitly, aspreviously described. For the present example, in this modality, imageI100 is marked with structure 52 vertex elements VE10, VE11, VE12, andVE13.

The construction parameter space includes nine unknown parameters forcamera C100 (six pose, one focal length, and two center-of-projection),three unknown shape parameters base_width, base_height, and base_depthfor structure 52, and nine spatial operator parameters (three each forrotation, translation and scale). For the default initialization of thefirst structure, the spatial operator parameters are fully constrained.To set a relative object scale, one or more shape parameters are set toarbitrary default values by the system or user. To set an absoluteobject scale, one or more shape parameters are set to of known realdimensions by the system or user. Parameter values may be imported intothe system as ancillary data 22 accompanying the import of an image 21.In the present example, parameter base_width is set to 1.0 units toestablish a relative scale. Intrinsic camera parameters may also be setto known calibration values by the system or user.

At step ST53, photogrammetric modeling engine 13 solves the parameterspace of all unknown geometry structure and camera parameters. In thepresent example, modeling engine 13 executes its reconstructionalgorithms to recover nine parameters defining camera C100 and twounknown and unconstrained shape parameters base_height and base_depth.FIG. 11( a) shows the construction prior to recovery of shape and cameraparameters. In FIG. 11( b), image I104 depicts structure 52 basegeometry back-projected into image I100 with unrecovered shapeparameters and from the unrecovered camera C100. In FIG. 11( c), cameraand shape parameters are recovered. In FIG. 11( d), image I105 showsstructure 52 geometry back-projected into image I100 with recoveredshape and camera parameters. The geometry now aligns with the image.

At step ST54 the system inquires as to whether geometry engine GE 15 isto evaluate geometric operators between nodes. If no, (or there is onlyone node, as is the case for the present example) the process proceedsto step ST56. Otherwise, the process proceeds to step ST55, where allunperformed operations are executed by GE 15. In the present example,the user elects to perform geometric operations after the addition ofeach new structure.

At step ST56 the system inquires as to whether additional structures areto be added to the present construction. If yes, process flow proceedsback to step ST51 with the selection and insertion of the nextstructure. Otherwise, process flow proceeds to step ST57.

At step ST57 the system inquires as to whether additional images are tobe added to the project. If yes, process flow proceeds to step ST50where a new image is loaded and process enables marking the new imageand corresponding marked features of the image with existing or newstructure elements. Otherwise, process flow proceeds to step ST58.

At step ST58, the scene graph is traversed and geometry engine 15executes all unprocessed geometric operators. If in all geometricoperators where already executes at step ST54, processing is complete.

In the present example, additional structures 51 and 53, and 50 areadded, so process flow cycles back through step ST51 three times.

At step ST51, structure 51 is instanced to represent the dome of theobject construction. The system inserts “dome” structure 52 as node N22in scene graph 375 of FIG. 8( c). Spatial operator S₂₀(X) in link L1between scene graph nodes N20 and N21 of scene graph 375 encodes thespatial parameters between structures 52 and 51. Variable parameters oftranslation, rotation, and scale are explicitly constrained to positionthe “dome” component relative to the base component, as shown FIG. 9.Link L1 contains the geometric boolean merge operator 61 specifying thatstructure 51 will be combined with base structure 52.

To position the dome on top of base structure 52, the midpoint of the ybounding extent of structure 51 and the maximum y-extent of structure52, are equated. To center it, the midpoints of the x and z boundingextents of the two structures are equated. As previously discussed, thesystem allows for many ways to specify constraints. For example, vertexelement VE30 at the center of sphere structure 50 could be directlyequated to the midpoint of the top face of structure 52.

Constraints are also placed on structure 51 shape parameters. The sphereshape parameter sphere_radius is set equal the base structure sizecbase_depth. To solve for the dome height sphere_radius2, point featureVF50 is marked in image I101 of FIG. 6( b) and is corresponded to vertexelement VE31 of structure 51.

At step ST53 modeling engine 13 solves for the dome height parametersphere_radius2. If previously solved parameters are not “locked”, theengine will optionally resolve them.

At step ST55, the boolean merge operation between nodes N20 and N21 isexecuted by geometry engine 15. FIG. 12( a) shows the construction priorto the geometric operation. FIG. 12( b) shows the construction after tothe geometric operation.

For the present example, the above process flow repeats twice, once forthe addition of box structure 53 and once for the addition of cylinderstructure 50.

Box structure 53 serves as the door opening of the arch assembly, asshown in FIG. 9( a). It is inserted as “door” node N22 in scene graph375 of FIG. 9( b).

To position the door, spatial operator S₂₁(X) in link L2 is set suchthat the minimum y-extents and midpoint x-extents of structures 53 andstructure 52, are equated. Link L2 contains the boolean differenceoperator 63 specifying that structure 51 will be combined with basestructure 52.

To constrain the depth of the door to that of its parent base, shapeparameters door_depth and to base_depth are equated. As shown in imageI101 of FIG. 6( b), edge features EF22, EF30, EF28, and EF29 are marked.These are corresponded to edge elements EE22, EE30, EE28, and EE29 inFIG. 9( a).

At step ST53 modeling engine 13 solves for the door_width and heightparameter door_width and door_height.

At step ST55, the boolean difference operation between nodes N20 and N22is executed by geometry engine 15. FIG. 12( c) shows the constructionprior to the geometric operation. FIG. 12( d) shows the constructionafter to the geometric operation.

At step ST51, structure 50 is instanced to represent the arch of theobject construction. The system inserts “arch” structure 50 as node N23in scene graph 375 of FIG. 9( b). Spatial operator S₂₂(X) in link L3between scene graph nodes N22 and N23 encodes the spatial parametersbetween structures 50 and 53. Link L3 contains the geometric booleanmerge operator 61 specifying that structure 51 will be merged with doorstructure 53.

Cylinder structure 50 corresponds to features in image I101 of FIG. 6.It is attached to the door by equating its midpoint y-extent with themaximum y-extent of door structure 53 and its midpoint x and z extentswith those of structure 53.

The width of the arch is to coincide with that of the door, so parametercyl_radiusA is equated to structure 53 parameter door_width. To solvefor cyl_radiusB, the height of the arch, feature point VF60 is marked inimage I101 of FIG. 6 and corresponded to vertex element VE35, shown inFIG. 4( a). The depth of the cylinder is to correspond to that of thebase and the door, so parameter cyl_length is equated to base structure52 parameter base_depth.

At step ST53 modeling engine 13 solves for the arch height parametercyl_radiusB.

At step ST55, the boolean merge operation between nodes N22 and N23 isexecuted by geometry engine 15. Given the hierarchy of the scene graphnodes, the arch structure is merged with the door structure. The mergedstructure is subsequently subtracted from the base structure. FIG. 12(e) shows the construction prior to the geometric operation. FIG. 12( f)shows the construction after to the geometric operation, which is thefinal construction. In the present example, with no more structures orimages to process, process flow proceeds through steps ST56 and ST57. Atstep ST58, GE 15 does no geometric processing since all such operationswhere performed at step ST55. Process flow therefore terminates.

The above construction example is only but one of many solid modelingstrategies for building the model of FIG. 12( f). For example, as analternative to preformed solid objects, one might employ extrusions andrevolutions instead. As shown in FIG. 13( d), the door structure couldbe implemented with the extrusion structure of FIG. 13( a). In thisfashion, the rectangular extrusion profile X10 would be constrained tothe front face base structure 52 in the same manner as was doorstructure 53. With straight line profile P10 orthogonal to the face ofstructure 52, an extrusion along its path produces the door structure,which is then subtracted from structure 52. Likewise, revolution ofprofile X40 around path P40, containing vertex element VE30 correspondedto vertex feature VF50 in image I101 of FIG. 6, produces a generateddome structure. FIG. 13( e) shows the final result.

Methods of Media Processing and Visualization

A set of system configurations and methods for processing andvisualization of media utilizing the media processing engine (MPE) aredisclosed.

The phantom cursor (PC) is an apparatus and process for acquiring a3-space camera solution relative to any planar facet of a scene orobject depicted in a 2D image. The PC is alternately utilized to solvefor structure constrained to a 3-space plane given a known camerarelative to the plane.

The PC apparatus comprises cursor graphics associated with 2D images, auser interface specification for creating and or modifying the cursorgraphics, and an underlying 3-space construction frame and a parametersolution process. The PC apparatus embeds the fundamental geometry andcamera construction and recovery processes of the MPE. This includes theannotation of 2-space imagery, the placement of and constraints on3-space structures and cameras, the correspondence of 2-space featuresto 3-space elements, and the recovery of unknown parameters. The“phantom” terminology is employed to denote that structure componentsformed by the PC mechanism may or may not contribute to a constructionmodel output.

The cursor graphics represent 2D-feature annotations to be associatedwith a 2D image. In the present embodiment, the phantom cursor graphicis an n-sided planar polygon in the plane of the image, consisting of nfeature vertices (points) and n feature line segments (edges). For agiven cursor graphic, the number n is either a built-in attribute of thesystem or is explicitly specified by the user. The system supports anynumber of simultaneous cursor graphics of varying dimension.

The 2D features of a PC graphic correspond to elements of a structureembedded in a 3-space construction frame. In the present embodiment, aquadrilateral structure embedded a reference plane, whose elements arecorresponded to quadrilateral features in the image plane of a camera,is sufficient for Media Processing Engine 11 to recover the camerarelative to the reference plane. Such a construction is also sufficientto recover the shape parameters of the structure embedded in thereference plane relative to a specified camera.

In the present embodiment, there are two primary ways that a cursorgraphic is instantiated in an image. When the dimension n of the cursorgraphic is known, a pre-formed cursor graphic of that dimension isinstanced. When the dimension is unknown in advance, an interactiveprocess is invoked, whereby the user interactively forms the cursor byindicating the corner feature points of the cursor graphics directlyonto the 2D image to which the cursor is associated.

FIG. 17( a) shows pre-formed quadrilateral (n=4) PC graphic PG1. PG1comprises four feature line segments F1, F2, F3, and F4. Theintersections of these feature line segments determine the four cornerfeature vertexes VF1, VF2, VF3, and VF4 of the main rectangle. Theextensions of the feature line segments beyond the intersections providea visual aid to assist the user in manipulating the cursor. PG1 is yetto be associated with a 2D input image. FIG. 17( b) shows a default3-space construction frame associated with the PG1. The presentembodiment defaults the 3-space construction frame to a standardCartesian coordinate system with x, y, and, z axes oriented asillustrated. Rectangular structure 69 embedded in the reference planerepresents the imaging structure. Default camera C1 is initialized tosome arbitrary internal and external parameterization. Element vertexVF1 is assigned as the first vertex of PS1 and is automaticallycorresponded to PG1 vertex feature VF1. Assuming a clockwise order ofthe remaining vertices of PS1, all the vertexes and line segmentsbetween PG1 and PS1 are automatically corresponded. These assignmentsand orientations are defaults and are fully alterable by the user or theapplication.

As an example of the processing and application of the PC, a plane inthe room scene of image I200 of FIG. 18( a) is recovered. FIG. 18( b)shows image I200 with PG1 superimposed. Floor plane 303 depicted in theimage is to be recovered. To recover a 3-space plane representing floor303 from image I200, the user interactively reshapes phantom graphic PG1superimposed within the image viewing window such that it appears to liein perspective on the depicted plane of the floor. FIG. 18( c) and FIG.18( d) show modified cursor graphic PG1 reshaped from its originalsquare shape to a quadrilateral in the 2D image plane. Thisquadrilateral is now the image feature annotation set that relates theprojection of the 3D reference plane and embedded structure constructioninto the image plane of the camera.

The 3-space construction of the present example is shown in FIG. 18( e).The x-z plane is designated by the application as the initial referenceplane defining floor 303 in image I200. The reference plane isrepresented by structure 310 embedded in the x-z plane. In general, theorientation of the initial reference plane is set by either theapplication or explicitly by the user. PC structure PS1 is embedded inplane 310 and by default is centered on the 3-space world origin. In thepresent example, structure PS1 is a 2-parameter rectangle. Camera systemC200 is attached to the input image I200 and is to be recovered. PME 13recovers camera C200 and phantom structure PS1 shape parameters relativeto fixed reference plane 310.

FIG. 19 shows a flow diagram of the PC process. Upon entry at stepST100, PC graphic PG1, construction reference plane 310, 3-space PCstructure PS1, and 3-space camera system C200 are established andinitialized.

At initialization, PC graphic PG1 feature segments F1, F2, F3, and F4and feature points VF1, VF2, VF3, and VF4 do not correspond to anyfeature of input image I200—the cursor is “floating” as shown in FIG.18( b). Unless explicitly overridden by the user, the systemautomatically corresponds PC graphic features to PC structure elements.In FIG. 18 PG1 line segments F1, F2, F3, and F4 are assigned to PS1 edgeelements E1, E2, E3, and E4, respectively. Alternate correspondences areequally valid as long as they maintain a proper clockwise orcounterclockwise feature to edge assignment. The system also allows theuser explicitly establish the correspondences if desired.

At step ST101, through user interface 12 and user-interactive input 25,the user interactively reshapes PC graphic PG1 superimposed on imageI200. In the current embodiment and for the present example, userinterface 12 allows the user to interactively “grab” line segment andpoint features to reshape the PG1 in a “rubber-banding” fashion. Cursorline segment features F1 through F4 and point features VF1 trough VF4are repositioned such that PG1 appears to lie in proper perspectivewithin the targeted plane depicted in the image. For the presentexample, this is the depicted floor plane 303. The PC mechanism does notrequire image feature annotations to correspond directly to particularfeatures seen in associated imagery. This allows the system to treatscenarios in which features in an image are incomplete, obscured, or donot exist. Execution of step ST101 establishes correspondence between PCgraphic PG1, input image I200, and camera C200. With the correspondencebetween the PC structure PS1 and PC cursor graphic PC1 established bythe system, the PC graphic PC1 is now considered “attached” as opposedto “floating”.

At step ST102, parameter constraints are set implicitly by system orexplicitly by user. If the plane recovery represents the firstgeometry-camera relationship established the PC automatically defaultsone dimension of structure PS1 to an arbitrary scale value. Thisestablishes implicit scale for the project. Alternately, partial or fulltrue scale may explicitly set by the user with known or assumed values.For example, for partial scale, the user sets one dimension of rectanglePS1 to an actual known dimension of the scene or object. For full scale,the user sets two dimensions. In general, for an n-sided phantomstructure, n shape parameters may be set. When explicit scale is to beset, the user will typically align PC graphic features to observedfeatures in the image that are of known (or to be guessed) dimensions.

At step ST103, photogrammetric modeling engine 13 is invoked to solvefor all unknown and unconstrained parameters of the overall PCconstruction. The shape parameters of PS1 as well as the intrinsic andextrinsic parameters of camera C200 are recovered.

Image Mosaics

Sometimes the field-of-view of a camera is not wide enough to capture anentire scene in one image. For such scenes, a wide angle lens might beused, but such hardware is expensive and can still produce insufficientresults. The system provides a processing modality that will mosaictogether any number of images shot from a camera from the sameviewpoint. This apparatus and method allows an individual to readytransform an ordinary camera into a wide-angle acquisition andvisualization system.

In mosaic mode, media processing engine 11 accepts two or more 2Ddigital images 24 or 21 under the control of user-interactive input 25through user interface 12.

An example of input 24 is a user downloading images from a digitalcamera directly onto the host computer system. An example of input 21 isa user downloading digital imagery from a conventional film developingservice (e.g. conventional 35 mm film) which offers digital outputformat and delivery directly over the Internet.

A flow diagram of the mosaic process is shown in FIG. 20. The processbegins at step ST200 with the establishment of a 3-space mosaicconstruction frame and imaging plane. The construction frame constitutesthe parametric 3D geometric representation of the mosaic. By default,the 3-space construction frame of the mosaic apparatus is the default PCconstruction of FIG. 17( b). This establishes the x-y (z=0) plane of thestandard Cartesian coordinate system as the mosaic imaging plane.

At step ST201 the user enters the first input image via 12. The firstinput image is established as the base image of the mosaic construction.Process flow then proceeds to step ST202 and a system query for entry ofanother input image. By definition, a mosaic comprises two or moreimages. If at step ST202 only one image has been loaded, process flowautomatically proceeds back to step ST201 for entry of another image. Ifat step ST202 no additional images are required, process flow proceedsto image annotation step ST203.

To relate and mosaic together any two images, the user identifies,within each image, one or more planar polygonal regions in common withthe other image. Such regions are marked in each image with the PCmechanism. Correspondences amongst regions and images are established byformation of PC groups, whereby each PC group creates and shares acommon geometric structure embedded in the mosaic imaging plane of the3-space construction.

At step ST203, the first PC group is established. The user identifies aregion common to two or more input images. Through user-interactiveinput 25 and interface 12 a PC is created and placed by the userclicking on n corner points of an identified n-sided region while thesystem “rubber-bands” out piecewise adjoining line segments. By default,the system maintains PC and group accounting by mode-all PC'sestablished during a given “form group” mode are equated. In general,groups may be set explicitly by the user or implicitly by anapplication.

An example of the user-interactive process at step ST203 is shown withthe set of input images of FIG. 21. The user inputs three images I300,I301, and I302 at steps ST201 and ST202. By choice of the user, imageI300 is entered first, thus becoming the base image. The user visuallyidentifies and selects the center “french door” region common to allthree images. FIG. 22 shows input images I300, I301, and I302 with PCgraphics superimposed. In image I300, the user marks four corner pointsof the identified region starting from the lower-left corner point VF5and proceeding in a clockwise order. A counter-clockwise layout couldhave been entered, as long as within a group, each PC annotation followsthe same clockwise or counter-clockwise orientation. This produces a PCgraphic PG2 with edge features F10 through F13 as shown. The systemautomatically extends the polygon edge line segments to span thedimensions of the image. This enhances accuracy and provides an visuallyaids the user's adjustments to the PC graphic.

The user may interactively adjust PC graphic features by moving the edgesegments and or the corner points through user-interactive input 25 andinterface 12. In image I301, the user marks the identified regionstarting from the same lower-left point orientation VF9 and proceedingin the same clockwise fashion, producing a PC graphic PG3 with edgefeatures F14 through F17. The same process is carried out with imageI302, starting with point VF13, producing a PC graphic PG4 with edgefeatures F18 through F21. In this example, the PC graphic of each imagecorresponds to the same image region-PG2, PG3, and PG4 are of the samegroup. With the user following a consistent data input orientation, thesystem automatically corresponds image I300 PC feature F10 to image I301PC feature F14 and image I302 PC feature F18, and so on.

In 3-space, the construction of a mosaic consists of a common imagingreference plane and structure representing that plane, a PC structurefor each PC group embedded in the imaging plane, and a camera systemassociated with each input image. At step ST204, the PC structure forthe first PC group is embedded in the reference imaging plane structure.

A construction for the present example with three input images I300,I301, and I302 is shown in FIG. 23. The mosaic imaging plane isrepresented by planar rectangular structure 50. Cameras C300, C301, andC302 correspond to images I300, I301, and I302 and PC graphics PG2, PG3,and PG4, respectively. At step ST204 the first (and only) PC structure,PS2, corresponding to PC group PG2, PG3, and PG4, is embedded in imageplane structure 50 with its centroid spatially constrained to 3-spacecoordinate origin 52. PS2 is a quadrilateral, corresponding to thedimension of each PC graphic in the group. PC structure PS2, shown ingreater detail in FIG. 24( a), is comprised of four vertices VE5, VE6,VE7, and VE8 and four edge elements, E5, E6, E7, and E8.

The shape of PC structure PS2 is determined by 8 independent parametersxa, ya, xb, yb, xc, yc, xd, and yd. The coordinates of the vertices ofPC structure PS2 are a linear combination of these shape parameters asprescribed in FIG. 24( b). In general, an n-sided structure contains 2nparameters. If the structure is constrained to be rectangular or square,then the numbers of shape parameters are 2 and l, respectively.

At step ST204, a base image and camera for the first PC group isselected. Unless set by the user or application, the base image defaultsto be the first image entered at step ST200. In the present example,this is image I300. In 3-space, the mosaic composition of a set ofimages requires finding the camera solution of each image relative to acommon image plane. By default, the base camera-image pair (C300-I300)is fixed such that its line-of-sight is orthogonal to the image plane(coincident with z-axis) and its point-of-view a fixed distance alongthe z-axis. In this configuration, the mosaic imaging plane and theimage plane of the base camera are parallel.

Given the default spatial orientation and relationship of the basecamera-image pair, the projection of the PC graphic of the base imageestablishes the correspondence and spatial orientation of PC graphicsfeatures with PC structure elements. For the present example, FIG. 24(c) shows how PC structure PS2 is corresponded to the PC graphic groupcomprised of PC graphics PG2, PG3, and PG4. Each edge element of PS2corresponds to 3 edge features. For example, edge element E5 correspondsto edge features F10, F14, and F18.

At step ST204 the shape parameters of the first PC structure are solvedby modeling engine 13. Once solved, the values of these shape parametersare locked.

At step ST205, an inquiry to determine if additional PC groups are to beadded. If yes, the process flow proceeds to step ST206, otherwise theprocess flow proceeds to step ST208.

At step ST206, the user adds one or more additional PC groups. At stepST207 a PC structure is automatically added to the mosaic imagingstructure for each additional group. These structures are constrained tolie in the imaging plane but are not explicitly constrained in position.

At step ST208, the shape and position parameters of all unsolved PCgroup structures and cameras are solved for by modeling engine 13. Inthe present example, all three input images are corresponded through aPC structure PS2. No additional PC groups are added. Cameras C301 andC302 are recovered by modeling engine 13 relative the solution of PS2and camera C300 at step ST204.

At step ST209, visual reconstruction engine 16 composes the set of inputimages into a single mosaic image. In mosaic mode, output 26 of mediaengine 11 is a single image composition of the input image set. Thecomposition of the input images is a projection of the images ontoimaging plane 50 from the respective recovered cameras of the inputimages. In the present embodiment, the base image is first projected andtexture-mapped onto the mosaic imaging structure. Subsequent cameraprojections are clipped against regions on the image planetexture-mapped, with only regions previously not texture-mappedrendered. The clipped images are composed using standard edge featheringtechniques. In general, any blending and composition methodology may beemployed.

Rendering Engine 17 displays the resulting composition. FIG. 25 imageI303 shows the resulting mosaic composition for the present example withof input images I300, I301, and I302. In addition to output 26, output27 delivers the mosaic content output to the network 10. A mosaicdatabase is three-dimensional and therefore the rendering of the mosaicmay leverage the 3D structure. For example, the entire mosaic imagingplane construction be rotated or translated in a 3D viewing operation.

Image Collages

A collage is a composition of a base (destination) image with one ormore ancillary product (source) images. The composition process entails“cutting” specified regions of source images and “pasting” them intospecified regions in the destination image. Examples of base imagesinclude single images representing scenes andior objects to be modifiedand image mosaics of such images generated by the system or by anexternal process.

In collage mode, media engine 11 accepts two or more 2D digital imagesfrom the user at data input 24 under the control of interface 12.Alternately, images are downloaded into the system at input 21 fromnetwork 10. The system outputs 2D image collage compositions 26 forrendering and viewing by the system, for storage, or sends 2D imagecompositions to network 10 through output 27.

Under the control of user-interactive input 25 and interface 12, thecollage process entails the user identification and annotation of “cutfrom” feature regions in a set of input source images and identificationand annotation of corresponding “paste to” feature regions in an inputbase image. A system solution for source and destination region geometryand camera systems relative to a common collage imaging plane enablesprojective composition of source collateral with the destinationcollateral.

A process flow diagram for image collage construction and composition isshown in FIG. 26. As an example, a collage is formed with scene imageI200 of FIG. 18( a) as a base image and two product images I500 and I502shown in FIG. 28. FIG. 29( a) shows the 3-space collage constructionframe for current example.

At step ST300, the process begins with the establishment of a 3-spacecollage construction frame and imaging plane. In 3-space, theconstruction of a collage consists of a common collage imaging plane andstructure, one or more structures embedded in the imaging planestructure representing destination regions, and a camera systemassociated with each input image. The 3-space construction frameconstitutes the 3D parametric geometric representation of the collage.By default, the 3-space construction frame of the collage apparatus isthe default PC construction of FIG. 17( b) with the x-y (z=0) plane asthe collage imaging plane. A shown in FIG. 29( a), planar rectangularstructure 600 is embedded in the plane and serves as the collage imagingplane structure.

At step ST301 a base image is entered through user interface 12. In thepresent embodiment, the process assumes a collage based upon a singlebase image. If additional scene images are desirable or required, themosaic process (or other means) are employed to produce a base imagethat is a mosaic composition of a multiplicity of scene images. Sourceimages may also be mosaic compositions. For the present example, sceneimage I200 is entered. For base image I200, associated camera C200 isestablished and constrained to a fixed 3-space position and poseparameterization. Its point of view is on the z-axis a fixed distance601 from the construction frame origin and its line of sight iscoincident with the z-axis and pointing toward the imaging plane 600.

At step ST302 a source (product) image is imported. In the presentexample, carpet image I500 of FIG. 28 is imported. In the constructionframe of FIG. 29( a), corresponding camera C500 is instantiated with anunsolved default parameterization.

At step ST303 the user annotates the base image and the current sourceimage as a PC group.

As previously disclosed, PC graphics are entered on a point-by-pointbasis or as a pre-formed graphic from built-in library 18.

For the present example, source image I500 is annotated with PC graphicPG5 to identify the source region of the current source image. Featurevertex VF1 is arbitrarily assigned first and the remaining threevertices are marked in a clockwise direction. Scene image I200 isannotated with PC graphic PG6 to identify the corresponding destinationregion in the base image resulting in FIG. 27 image I400. For PC graphicPG6, feature vertex VF5 is marked first and the remaining vertices aremarked in a clockwise direction. The PC process corresponds VF1 to VF5and the remaining features in the PC group according to the given inputtopology.

At step ST304 a PC structure for the current PC group is embedded in thecollage imaging plane and structure. By default, the dimension of the PCstructure for the group is the number of vertices of the source regionPC. In the present example, the dimension is four.

Given the fixed position and pose of the base image camera, the PCstructure elements are automatically corresponded to base image PCgraphic features through projective topological preservation. The PCstructure elements are automatically corresponded to the source PCgraphic features since the base and source PC graphic correspondenceshave been established. Modeling engine 13 solves for the unknownvariable shape parameters of the PC group structure based on itscorrespondence with the fixed base camera. After the group PC structureparameters are recovered, they are locked. Modeling engine 13subsequently recovers the variable intrinsic and extrinsic parameters ofall or selected source cameras.

In the present example, PC structure PS3 is placed in imaging planestructure 600, as shown in FIG. 27. PC structure PS3 vertex element VE10is corresponded to PC graphic PG5 vertex feature VF1 and PC graphic PG6vertex feature VF5. PS3 contains 8 independent shape parameters likestructure PS2 of (a). These variable shape parameters are recoveredbased on the fixed position and pose of camera C200 attached to baseimage I200. With these structure parameters constrained, camera C500attached to source image I500 is recovered.

At step ST305, the system queries as to whether additional source imagesare to be processed. If yes, process flow proceeds back to step ST302and the input of another source image. If no, process flow proceeds tostep ST306. In the present example, process flow proceeds back to stepST302 with the input of source image I502 of FIG. 28. At step ST303,FIG. 27 base image I400 is annotated with PC graphic PG7. Vertex featureVF9 is entered as its first vertex. Source image I502 is annotated withPC graphic PG8 with vertex feature VF13 as its first entry. At stepST304 PC structure PS4 is inserted in imaging plane structure 600.Again, the structure placed contains 8 independent shape parameters likethe structure PS2 of (a). The system corresponds PC graphic PG7 vertexfeature VF9 to PC graphic PG8 vertex feature VF13 and PC structure PS4vertex element VE20. PC structure PS4 variable shape parameters arerecovered by modeling engine 13 based on the fixed position and pose ofcamera C200 attached to base image I200. With these structure parametersconstrained, the variable intrinsic and extrinsic parameters of cameraC502 attached to source image I502 are recovered.

At step ST306, reconstruction engine 16 prepares the collage databasefor composition and rendering. All source images are clipped againsttheir respective PC graphic regions. This constitutes the “cut” portionof the “cut-and-paste” collage processing paradigm. By default, theinterior region of a PC graphic is retained as the source imagery to becomposed with the base imagery. Alternate selections are made by theuser or the application program. In general, any combination of clipregions can be selected. In the present example, image I500 is clippedagainst PG6 and the interior region of the PC graphic is retained.Likewise, image I502 is clipped against PG8 and the interior region ofthe PC graphic is retained.

At step ST307, Rendering Engine 17 composes and renders the base imagewith all source images. Clipped source image regions are composed withtheir respective base image destination regions by projecting them ontothe base imaging plane through their recovered cameras.

In the present example, base image I200 is projectively texture-mappedonto image plane 600 through camera C200. Then, source image I500 isprojectively texture-mapped onto it corresponding structure PS3 throughcamera C500 and source image I502 is projectively texture mapped ontostructure PS4 through camera C502. Upon projection, source regions maybe combined with destination region using any desired blendingoperation. In the present example, source pixels replace destinationpixels. The final collage is FIG. 30 image I401.

Rendering engine 17 implements multiple image layer compositiontechniques to resolve hidden surface and hidden object situations duringcollage composition. Commonly available image region selectionalgorithms and techniques are incorporated to generate masking layers.These layers are known as alpha channels. During composition, thesemasking layers determine which pixels of source and destination imagerycontribute to the final collage composition. Rendering engine 17processes alpha channel images as an integral component of itsprojective composition methods. Rendering engine 17 utilizes some of thesame techniques in the rendering of full 3D constructions.

As an example of alpha channel processing, FIG. 31 shows room sceneimage I600 into which television image I601 will be collaged. Image I602of FIG. 32 shows a collage composition of images I600 and I601. In imageI602, source “television” pixels of image I601 are seen obscuring“couch” pixels, a visibly unrealistic and undesirable result. This areais pointed to as region R1. Image I603 of FIG. 32 shows an alpha channelimage generated by the user with a system incorporated image regionselection tool or a standalone image processing tool such as [4]. Inimage I1603 the “couch” region (black pixels) is isolated from theremainder of the scene (white pixels). In the rendering process,rendering engine 17 projects alpha mask image I603 from the perspectiveof the recovered scene image camera onto the collage imaging plane priorto rendering the source region of image I601. Upon rendering image I601,destination pixels are replaced only if their corresponding alpha imagepixels of image I603 are white. Image I604 of FIG. 33 shows the finalresult, with the couch pixels preserved. Image I604 also showsutilization of the alpha channel image I603 to assist in modifying thedepicted color of the couch.

3D Scene Construction

The above-described mosaic and collage methods form image compositionsbased on 3-space constructions with a single imaging plane. The 3D sceneconstruction methods disclosed below extend these concepts toconstructions that are assemblages of multiple planar and volumetricstructures in 3-space. Scene constructions are assembled from one ormore input images.

Scene construction methods are presented in the context of anapplication specific implementation of the current embodiment of thesystem. The application example given is targeted for the constructionof a 3D architectural scene. An example room interior construction isshown. This application and associated methods are just one example ofthe general applicability of the MPE to a vast range of scene and objectconstruction scenarios.

A process flow diagram of a general 3D scene construction process isshown in FIG. 34. As an example, a 3D geometric construction for thescene depicted in image I700 of FIG. 35 is created. The resultinggeometric construction is shown in FIG. 36.

At step ST400 the user imports a scene image through user interface 12.At step ST401 the system determines if the current input image is thefirst. If it is, then process flow proceeds to step ST402. Otherwise,process flow proceeds to step ST403.

At step ST402 a PC process is initiated for the recovery of an initialscene reference plane and structure and camera system for the currentscene image. The present application is pre-programmed to beginconstruction of a room environment from a root floor plane. This“floor-up” method defaults the horizontal x-z plane of the PC Cartesiancoordinate system as the floor reference plane. In general, anapplication requiring only the recovery of a wall surface mightinitialize the y-z or x-y plane as the initial reference plane.Alternately, the user may explicitly specify the orientation of theinitial reference plane.

By default, a planar rectangular structure is automatically placed bythe system to represent the reference plane. The reference structurecontains two variable shape parameters set by the system atinitialization. The values for these parameters are selected such thatthe reference plane is large enough to encompass the field-of-view ofthe current scene camera. Initially, the size parameters are set toarbitrarily large numbers. These values are either explicitly modifiedby the user or are procedurally modified by the system once thefield-of-view of the designated scene camera is recovered. In the lattercase, the application determines if the geometry of the structure isclipped by the image window of the camera, when back-projected into thecamera. If so, the system enlarges the geometry until this condition iseliminated. This procedure thus ensures that the size of the structureis large enough to span the extent of the view port of the camera.

To recover the floor plane geometry and scene camera parameterization, aPC graphic is placed by the user in the main scene image and acorresponding PC structure is placed in the default floor referenceplane. The PC process is executed with modeling engine 13 solving forthe variable shape parameters of the PC structure and intrinsic andextrinsic variable parameters for the camera associated with sceneimage. In the PC process, the user enters known, guessed, or measuredvalues for PC structure shape parameters. A scene takes on true-to-scaleproportions if actual measured values are provided.

For the present example, at step ST400 scene image I700 is imported.This is the first input image and process flow proceeds to step ST402.At step ST402, planar rectangular structure 900 from built-in library 18is inserted by the system in horizontal x-z plane of the coordinateframe. Within structure 900, a planar rectangular structure from library18 is embedded as PC structure PS10. PS10 is spatially constrained to becentered at the origin of the coordinate frame. PS10 contains twovariable shape parameters S1 and S2. Camera C700, corresponding to imageI700, is instanced with unsolved default parameters. PC graphic PG10 isplaced in scene image I700, as shown in FIG. 35. Throughuser-interactive input 25 and interface 12, the user interactivelyshapes PC graphic PG10 in scene image I700 to appear to lie in thedepicted floor. FIG. 36 shows the 3-space construction with floor planestructure 900, PC structure PS10, and camera C700 established by the PCprocess. PC graphic PG10 features F81, F82, F83, and F84 correspond toPC structure PS10 elements E81, E82, E83, and E84, respectively.

The scene construction is calibrated to real world dimensions throughPS10 variable shape parameters S1 and S2. In image I700 the user hasaligned PG10 such that dimension S1 corresponds to the width of thedepicted hallway entrance and S2 corresponds the distance between thewall of the hallway entrance and the end of the fireplace. The usersupplies known measured values or guessed values for these parameters.Modeling engine 13 solves for all unknown parameters of the PCconstruction. Floor structure 900 and scene camera C700 intrinsic andextrinsic parameters are recovered relative to established structure 900and calibrated to produce the scale dictated by the submitted shapeparameters of PC structure 900. FIG. 37 shows the construction scenegraph 275 with structure 900 in “floor” node N10.

At step ST403, the system queries as to whether additional structuresare to be added. If yes, process flow proceeds to step ST404. Otherwise,process flow proceeds to step ST407.

At step ST404 the next structure of the 3-space construction is selectedand placed. Structure selection is explicit by the user or implicit bythe application.

In the present example, the user places a wall for the “left” side ofthe room. Planar rectangular structure 902 instanced from built-inlibrary 18 is selected by the user as a “wall” object. The presentapplication is pre-programmed to place “wall” structures in anorthogonal orientation relative to “floor” structures and to build-upfrom floor structures. Structure 902 is inserted into scene graph 275 as“left-wall” node N12, a child of floor node N1. The system constrainsthe minimum y-extent of plane 902 to the surface of floor planestructure 900. As with reference plane 900, structures added to thescene are initialized to default or procedurally generated sizes.Structure 902 has two shape dimensions S3 and S4, corresponding towall_width and wall_height, respectively. Unless explicitly overriddenby the user, the application sets wall_width and wall_height to valueslarge enough that the extents of these structures cover the field-ofview of the 2D image window as viewed by the scene camera C700.

At step ST405, the full position and shape parameterization of an addedstructure is resolved.

To determine the complete set of placement and dimensions parameters ofan added structure, the user annotates features in the scene image thatcorrespond to elements of the new structure. In a typical application,the system will request information from the user specific to thecontext of the construction underway. In the present example, to solvefor the exact positioning of structure 902 relative to structure 900,the system requests the user annotate input image I700 with an edgefeature line segment that indicates where the wall and floor meet. Inimage I700, the user annotates line segment F70. The system knows thisedge element belongs to a “floor-wall” juncture, so image feature F70 isautomatically corresponded to structure 902 edge element E70. Given thatfloor plane 900 and camera C700 are known, the F70-E70 correspondencepair is sufficient to fully place structure 902. The system then queriesthe user to provide image annotations for the left, right and top edgesstructure 902 to resolve its shape parameters. If none are provided, asis the case here, then default or system procedurally derived dimensionsare retained.

At step ST406, modeling engine 13 solves for unknown parameters of theconstruction. In the present embodiment, the system locks all previouslysolved for parameters. As an option, the system allows for all or selectgroups of solved parameters to be re-solved. In the present example, atthe insertion of wall plane 902, parameters pertaining to referenceplane 900 and camera C700 are locked. The unknown and unconstrainedparameters of spatial operator S₁₂(X) in the link between scene graphnodes N10 and N12 are recovered.

Process flow returns to step ST405 and the system queries as to whethermore structure are to be added. In present example, two more structures,planar “wall” structure 903 and volumetric “fireplace” box 904, areadded. Planar rectangular structure 903 is placed orthogonal to floorstructure 900. The position of plane 903 relative to plane 900 isestablished with user-supplied feature annotation F71 in image I700.Image feature F71 is automatically corresponded to structure 903 edgeelement E71. The user also annotates image feature edge segment F72corresponding to structure 903 element E72 to place the right edge ofthe wall 903. Structure 903 is inserted into scene graph 275 as“back-wall” node N11, a child of floor node N10, with spatialpositioning given by spatial operator S₁₁(X). The next structure addedto the construction is three-parameter volumetric box structure 904,which becomes “fireplace” node N13 in scene graph 275, a child ofleft-wall node N12. By default, the application constrains the minimumy-extent of the box structure to floor structure 900 and set“box-height” dimension S5 equal to the height value of wall structure903. The application also sets the minimum x-extent of structure 904 tothe plane of left-wall structure 902, constraining the back of thefireplace to the face of the wall. User placed image feature annotationsF73 and F74, corresponding to structure elements E73 and E74, determinethe width and depth dimensions of fireplace structure 904. Modelingengine 13 solves for all remaining unconstrained and unsolvedparameters, including spatial operator S₁₃(X) in the link between scenegraph 275 nodes N12 and N13.

A scene construction contains one or more scene images. The user mayelect to annotate an image at any time, in which case process flowproceeds to step ST405. Alternately, at step ST407, the system queriesthe user as to whether a scene image is to be annotated. If affirmative,process flow proceeds to step ST5. If negative, process flow proceeds tostep ST408, where the system queries the user as to whether a sceneimage is to be added to the construction. At step ST408, if affirmative,process flow proceeds back to entry point step ST400. Alternately, theuser may add scene images at any time, at which point process flowproceeds to step ST400. The addition of an image also means the additionof a camera system attached to the image. At step ST400, upon user entryof the image through interface 12, process flow proceeds to step ST403through step ST402. At step ST408, if negative, process flow proceeds tostep ST409.

At step ST409, with all scene structures specified, placed, and unknownstructure shape, spatial positioning, and camera parameters resolved,the system performs all geometric operations on the scene construction.The scene graph of the scene construction is traversed and the geometricoperations to be performed between structures are extracted from thelinks of the scene graph and are executed by GE 15. A traversal of FIG.37 scene graph of 275 shows that all links between structure nodescontain boolean union operator 60. The scene construction of FIG. 36depicts the scene 3D construction after traversal of the graph andexecution of all union operators.

In the present embodiment, there is only one 3D construction frameprocessed at a time. The existence of multiple image-camera systemsmeans that image feature annotation sets of different images maycorrespond to common structure elements. The parameter space to besolved is increased by the parameterization of additional cameras.

Intelligent Objects and Intelligent Scenes

Intelligent objects are object constructions whose functional componentsare structurally modeled and subject to physical simulation throughvariation of the variable parameters of their composition. An example ofan intelligent object is a cabinet construction containing a drawerconstruction that is parameterized to slide open and close.

Intelligent scenes, analogous to intelligent objects, are sceneconstructions whose functional components are structurally modeled andsubject to physical simulation through variation of the variableparameters of their composition. An example of an intelligent scene is aroom interior construction containing a door construction that isparameterized to swing open and close.

Media Integration Methods

The system provides seamless integration, visualization, and simulationamongst the various construction modes and types. This includes theintegration of image mosaics, image collages, 3D scene constructions,and 3D object constructions. Constructions are integrated within asingle project or as the coalescing of a number of separate projects.The latter scenario is typically implemented using a client-serverprocessing model, whereby construction components are interchangedbetween a multiplicity of processing nodes and projects.

Central to media integration processing is the merger and manipulationof construction databases comprised of scene graph and ancillary datastructures. FIG. 39 illustrates a media integration and simulationprocess flow.

At step ST900, the system queries as to whether a valid scene graphdatabase resides in unit 14. If true, process flow proceeds to stepST904 and the import of an insertion database. If not, process flowproceeds to step ST901 where a decision is made whether to construct anew scene database or load on existing saved scene database. If the userelects to create a new database, process flow proceeds to the 3D sceneconstruction process at step ST902. Otherwise, process flow proceeds tostep ST903 and the import of a scene database into unit 14.

At step ST904 an object database is imported and pre-processed. Importeddatabases range from complete projects that include imagery, geometryand camera models, and ancillary data to sparse projects that mightinclude only one of these components. For example, the receivingapplication might import imagery of an object but not the objectgeometry; depending on the application configuration, geometry for aconstruction related to an imported image may be internally sourced frombuilt-in library 18. This reduces the amount of data transferred overnetwork 10.

At step ST905 the method of object placement is selected. The placementof an object is either under the control of the application or thediscretion of the user. If initial placement is not under the control ofthe user, process flow proceeds to step ST907 where objects are assignedan initial position within the 3-space coordinate system procedurallydetermined by the application program. Otherwise process flow proceedsto step ST906 where the type of user-interactive object placement isselected.

Object placement is established by the user object with apoint-and-click mouse interface through user input 25 and interface 12.The anchoring of source and destination constructions is accomplished byselection and correspondence of features of source images andiorelements of source models to features of the destination scene imagesandior elements of destination scene models. For the source, the userinteractively selects one or more image feature annotations or objectelements as anchor reference points. For the destination, the userinteractively selects geometric elements of the scene model, existingscene image feature annotations, or places new image feature annotationsin one or more destination images. Modeling engine 13 solves for allunknown and unconstrained parameters defining the shape and spatialinterrelationships between merged construction components.

Scale factors between source and destination models are established bythe method of placement employed and the known and recovered values ofthe established parameter space. In present embodiment, the systememploys two primary methods of placement, true-to-scale andforce-fit-scale. True-to-scale processing is based on the use ofexplicitly known values of source and destination model parameters.Force-fit-scale processing is based on the use of implicit and recoveredvalues of source and destination model parameters governed by placementconstraints.

At step ST906 the type of user-interactive placement is selected. If the“explicit scale” processing is selected process flow proceeds to stepST908. Otherwise process flow proceeds to “force-fit scale” processingat step ST909.

At step ST908 true-to-scale object insertion is executed. In this mode,source and destination model dimensions are known or assumed. To mergemodels of known dimension, the user identifies and equates feature andelement anchors in source and destination imagery and geometry. Modelingengine 13 solves for the values of unknown spatial operators and cameramodels. The absolute scale between merged source and destination modelsis preserved. A typical use scenario of explicit scale is when a sceneof known true dimensions is inserted with an object of known truedimensions.

At step ST909 force-fit scale object insertion is executed. In thismode, a destination region is specified which allows modeling engine 13to size as well as position the source model into the destination model.The scale factor between merged source and destination constructions isestablished by the proportions of the destination region. A typical usescenario for this method is the insertion of an object of unknowndimensions into a scene.

As an example of the overall process flow of FIG. 39, the integration ofthe rug object depicted in image I500 FIG. 28 within scene I200 of FIG.18( a) is executed.

At step ST900 a new scene database is constructed. Process flow proceedsto step ST902 and the 3D construction process of FIG. 34 is executed. Inthat process, at step ST402, the PC construction process of FIG. 18 isexecuted. Analogous to FIG. 18( e), the 3-space construction of FIG. 42(c) is produced, with recovered floor plane structure 700, embedded PCstructure PG20 and recovered camera model C200 for image I200. Datastructures unit 14 contains scene graph 575 with a single node N100containing floor structure 700, as shown in FIG. 42( a). The scene isscaled to true measured dimensions through the PC process, with the PCgraphic aligned to known image feature reference points and the actualmeasured dimensions of those features entered for the correspondingparameters of the PC structure.

At step ST904 an imported object database comprises rug source imageI500 of with corresponding PC graphic PG6, and rug shape parametersrug_length and rug_width. The present application example ispre-configured to import “rug” databases. As such the system isconfigured to internally supply planar rectangular structures frombuilt-in library 18 for “rug” object constructions. With the import ofimage I500, camera model C500 is established. In the present example, nocamera model parameters are imported with image I500; camera C500 isinternally initialized with no known model parameters assumed. Theapplication automatically assigns planar rectangular structure 750 frombuilt-in library 18 to incoming PC graphic PG6, establishing thegeometric model of the rug object. Vertex element VE20 of objectstructure 750 is identified as the correspondence to PG6 vertex featureVF5. A direct correspondence between all PG6 edge and vertex featuresand structure 750 edge and vertex elements is established.

At step ST905, the user elects to interactively place the rug object. Atstep ST906 the user elects to place the rug using its imported truedimensions rug_length and rug_width. At step ST908 the user placescross-hair image features 933 in scene image I200 to establish thedestination anchor point for the rug object on the depicted floor, asshown in image I800 of FIG. 40. For the source anchor point, the userselects PC graphic PG6 feature point VF5 directly on image I500 of FIG.28, or vertex VE20 directly on object geometry 750. The application alsotexture maps image I500 onto geometry 750, allowing the user to selectVF5 and VE20 concurrently and directly from a 3D texture-mapped modelpresentation. The selected source and destination anchor points are thenequated, completing the source-to-destination correspondence and theexecuting the insertion is executed. Modeling engine 13 solves for allunknown camera and spatial operator parameters to place rug structure.The imported rug shape parameters rug_length and rug_width explicitlyspecify the true size of the rug object within the scene of knowndimensions.

FIG. 42( c) shows rug object structure 750 inserted and constrained tofloor structure 700. Two cameras are shown. Camera C200 for scene imageI200 and camera C500 for rug image I500. In FIG. 42( b), node N101containing structure 750 is inserted into scene graph 575 as a child offloor node N100, reflecting the insertion of the rug object into thescene. The spatial relationship between structures N100 and N101 isgiven by the spatial operator S₁₀(X) in the link between the nodes. Thevalues for the variable parameters of S₁₀(X) are established by explicitgeometric constraints and computation of modeling engine 13. In thepresent example, they translation component of S₁₀(X) is set to zero,constraining structure 750 to lie in the plane of parent structure 700.The x and z translation and y rotation components of S₁₀(X) arerecovered by modeling engine 13 to place the rug at anchor point 933, asshown in image I801 of FIG. 40.

An alternate example demonstrating the force-fit-scale processing ofstep ST909 is disclosed. The method combines destination and sourcemodel creation and integration through a shared PC process. Combined isthe PC process for finding a camera model and reference plane in a3-space construction for an input scene image and a “force-fit”procedure for merging additional 3-space constructions. The userinterface procedure is similar to that of the disclosed 2D image collageprocess.

The process is exemplified with the previous example of integration ofthe “rug” object of FIG. 28 within scene I200 of FIG. 18( a). At stepST900 process proceeds to step ST901. At step 901, the user elects tocreate new geometry and process flow proceeds to step ST902.

At step ST902 a destination reference plane is selected by the user anda PC process is initiated for the recovery of the initial destinationscene reference plane, structure, and camera system relative to thecurrent scene image. The application could be more specificallytailored-such as a “place rugs on floors” or a “place pictures on walls”application, in which case the destination orientation is a built-inattribute of the application. A PC graphic is placed in the scene imageand shaped by the user to define the destination region into which anobject to be inserted. A corresponding PC structure is placed in theselected reference plane and structure. FIG. 43 shows the 3-spaceconstruction.

At step ST902, the user explicitly indicates that the destination of anobject is a horizontal “floor” plane and the application defaults thehorizontal x-z plane of the PC Cartesian coordinate system defaults asthe floor reference plane. Planar rectangular structure 800 from library18 is inserted by the system in horizontal x-z plane of the coordinateframe. If, for example, the picture product of FIG. 28 image I502 is tobe placed within a vertical wall structure in the same scene, the userwould explicitly specify a vertical “wall” plane and the applicationwould default the horizontal y-z plane of the PC Cartesian coordinatesystem defaults as the wall reference plane. Scene image I200 of FIG.18( a) is annotated with PC graphic PG5 to identify the destinationregion in the scene image, as shown in image I400 of FIG. 27. Featurevertex VF1 of PG5 is marked first and the remaining PG5 vertices aremarked in a clockwise direction. Within structure 800 planar rectangularPC structure PS30, corresponding to PG5, is placed centered about thecoordinate system origin. Camera C200 of destination scene image I200 isinitialized with unsolved default parameters. Through user-interactiveinput 25 and interface 12, the user interactively shapes PC graphic PG5in scene image I200 such that the PC graphic region represent theoutline of the region to be occupied by a source object or components ofa source object, relative to the reference structure, and as viewed inperspective.

At step ST904 an object database is imported and pre-processed. In thepresent example, the imported object database consists of rug productimage I500 of FIG. 28 and corresponding PC graphic PG6. Values for shapeparameters rug_length and rug_width may or may not be imported. Nocamera model parameters are imported with image I500. The applicationexample is pre-configured to import “rug” databases. As such, the systemis configured to internally supply planar rectangular structures frombuilt-in library 18 for “rug” object constructions. In “force-fit” mode,the application defaults to using the current PC structure geometry. Inthe present example, this is PS30. The application automatically assignsplanar rectangular structure PS30 to incoming PC graphic PG6,establishing the geometric model of the rug object. Vertex element VE30of object structure PS30 is identified as the correspondence to PG6vertex feature VF5. A direct correspondence between all PG6 edge andvertex features and structure PS30 edge and vertex elements isestablished.

At step ST905 the application is programmed to proceed to step ST906 andthen to step ST900. At step ST909 “force-fit” processing is executed.The user corresponds one or more image features of source PC graphic PG6to one or more features of destination PC graphic PG5 or to one or moregeometric elements of PC structure PS30. In the present example, theuser clicks on PG6 vertex feature VF5 and then on PG5 vertex feature VF1to fully establish the source region to destination regioncorrespondence. Modeling engine 13 solves for the variable shapeparameters of the PC structure PS30 constrained within plane 700 andintrinsic and extrinsic variable parameters for cameras C200 and C500such that rug source region PG6 is fits into destination region PG5.

The relative scale between the rug and the scene is set by theannotation of the rug outline in the scene. The overall scale of thescene and inserted object is determined by the constrained or recoveredvalues of PS30 variable shape parameters. This scale may arbitrary, ifarbitrary default values are established for the parameters. This scalemay also be based on known imported dimensions of either the scene orthe rug. In the latter case, in the event the known true dimensionscorresponding to the annotation region in the destination image coincidewith the known true dimensions of the source region, the force-fit scalebecomes a true-to-fit scale.

Interactive Media Simulation Methods

Interactive visual and physical based simulation operations can beprovided between all modes and types of scene and object constructions.In general, the system can incorporate visual simulation features andtechniques available to a 3D computer graphics system.

Simulation is controlled by the manipulation of variable parameters ofconstruction through user-interactive input 25 through interface 12 orapplication programming. For example, the variable parameters of spatialoperators S(X) linking geometric structures are static or dynamic. Whenstatic, spatial operators specify rigid spatial positioning betweencomponents of an object or scene. When dynamic, spatial operators enableand specify the simulation and animation of functional components ofobjects and scenes. Another example is the variation of structure shapeparameters. This enables simulation features such as a review of varioussizes and shapes of an object within a scene construction.

FIG. 38( a) shows an example intelligent object assembly constructed bythe system. The assembly contains dynamic spatial operators. The modelassembly consists of main box structure 90, box cover structure 91, andbox drawer structure 92. The object assembly is encoded as scene graph300 with structure 90 in node N15, structure 91 in node N16, andstructure 92 in node N17. Spatial operator S₆(X between nodes N15 andN16 specifies the spatial relationship between the box and its cover.Spatial operator S₇(X) between nodes N15 and N17 specifies the spatialrelationship between the box and its drawer. Composed within operatorS₆(X) is cover_rotate_z parameter 94 that specifies rotation of thecover structure about the z-axis linking the cover and the box. Composedwithin operator S₇(X) is draw_trans_x parameter 93 that specifiestranslation of the drawer structure along the x-axis relative to thedrawer coordinate frame. Physical-based modeling attributes are alsoattached to the spatial operators. Variable parameters cover_rotate_z 94and draw_trans_(—×93) are manipulated through user-interactive input 25and interface 12 and application programming to simulate thefunctionality of these components of the model assembly.

Interactive simulation functions of the system include the spatialrepositioning of scene models and object models relative to each other.As shown in FIG. 41, the rug object of FIG. 28 image I500 isinteractively repositioned within the floor plane of the scene tosimulate various placement scenarios. The scene is represented by scenegraph 575 of FIG. 42( b). Repositioning of the rug object isaccomplished by interactive variation of the variable parameters ofS₁₀(X) while retaining its fixed constrained parameters. FIG. 40 imageI801 shows the rug within the scene at its user established anchor point933. FIG. 41 images I802 and I803 are two display frames rendered fromrendering engine 17 showing the rug object displaced along the floorfrom its initial anchor point 933.

Client-Server System Model

Disclosed is a client-server processing and communication model suitablefor deployment of the MPE and methods over a wide range of network-basedapplications. The MPE and methods are not limited to such animplementation.

As shown in FIG. 49, one or more client processing nodes and one or moreserver processing nodes are connected to a computer network, such as theInternet. A client node CN100 is a computer system or other networkappliance connected to a computer network. It is capable ofcommunicating with one or more server nodes and other client nodes onthe same network. A server node SN200 is a computer system or networkappliance connected to a computer network. It is capable ofcommunicating with one or more client nodes and other server nodes onthe same network. Client nodes and server nodes embed MPE 11.

Client node CN100 receives and sends and digital information 802 fromand to network 10.

Digital information 802 received from network 10 is comprised of 2Dimages 21, 3D geometric models 20, and ancillary data 22. Digitalimagery 21 is downloaded to a client node from a system server node,another system client node, or any other node on network 10. Geometricmodels 20 are parametric and non-parametric, and downloaded from aserver node SN200 or other client node CN100 and generated by 11 atthose nodes. Parametric models generated by 11 are structure assemblagesinterchangeable between client and server nodes. Parametric andnon-parametric models generated by other means, such as other modelingsoftware programs or hardware scanning devices, may also be downloadedfrom any network node at input 20. Data 802 sent to network 10 iscomprised of user. 2D images 24 and user 3D parametric models 23.Digital input 804 is digital information a client node receives from auser. This input includes user interactive input 25, user digital images24 imported from sources such as digital cameras and analog-to-digitalscanners, and 3D geometric models 23 imported from external medium suchas CDROM. It also includes system project databases and ancillary data28.

A server node SN200 receives and sends digital information 803 from andto network 10. Digital information 803 received from network 10 iscomprised of 2D images 21, 3D geometric models 20, and ancillary data22. Digital imagery 21 is downloaded to a server node from a systemclient node, another system server node, or any other node on network10. Geometric models 20 are parametric and non-parametric, anddownloaded from a server node or other client node and generated by 11at those nodes. Parametric and non-parametric models generated by othermeans, such as other modeling software programs or hardware scanningdevices, may also be downloaded from any network node at input 20.Digital information 803 sent to network 10 is comprised of user 2Dimages 24, and user 3D parametric models 23. A server node receivesdigital information 805 from a user. This includes user interactiveinput 25, digital images 24 imported from sources such as digitalcameras and analog-to-digital scanners and 3D geometric models 23imported from external medium such as CDROM. It also includes systemproject databases and ancillary data 28.

A client node executes client-side application software CA based on theMPE and processing methods disclosed. In a typical configuration, aclient node downloads CA from a server node on network 10 or from otherdata storage and delivery medium such as CDROM. The client-sideapplication program CA embeds some or all of media processing engine 11capabilities and features. CA is capable of generating, composing, andvisually rendering 2D image mosaics, 2D image collages, and 3D objectmodels, 3D scene models from 2D digital images based on the disclosedsystem and methods. CA is capable of dynamic visual and physical-basedsimulation of 3D object and scene models generated within the node,received from the network or other sources, and compositions of nodegenerated and node imported content.

A server node executes server-side application software SA based on theMPE and processing methods disclosed. The server application program SAembeds some or all of the media processing engine 11 capabilities andfeatures. SA is capable of generating, inter-composing, and visuallyrendering 2D image mosaics, 2D image collages, 3D object models, 3Dscene models from 2D digital images. SA is capable of dynamic visual andphysical-based simulation of 3D object and scene models generated withinthe node, received from the network or other sources, and compositionsof node generated and node imported content.

In a typical configuration, server nodes are responsible fordisseminating client-side application software CA components of 11executed by client nodes to client nodes. The components and type ofprocessing carried out by individual client or server nodes and the datatransactions between client and server nodes is a function of the targetapplication.

E-Commerce Merchandise Visualization and Information System

A product visualization, simulation, and information communicationsystem will now be described. Based on the media processing engine(MPE), methods, and client-server processing and communication model,the disclosed merchandising system connects product consumers, (e.g.shoppers, buyers) with product purveyors (e.g. manufacturers,retailers).

Users can visualize actual product application environments, into whichpurveyor product representations are seamlessly integrated, visualized,and simulated. The system also allows shoppers to generate 2D imagecompositions and 3D constructions of products directly from 2D imageryin the event that such representations are not already available.

The system may operate as a stand-alone application or be integrated asa component in an existing online e-commerce system.

The described embodiment of the e-commerce merchandising system is shownin FIG. 50. In a client-server configuration over network 10, productshoppers are associated with client nodes CN100 and product purveyorsare associated with server nodes SN200. Client and server processingnodes may also operate in stand-alone mode, removed from network 10.

In the context of a merchandising system, client nodes CN100 are calledSHOP nodes and product purveyor nodes SN200 are called SELL nodes. As anexample deployment scenario, SELL nodes are the e-commerce web sites ofproduct manufacturers, retailers, and the likes, and SHOP nodes areconsumers and the like with an online personal computer or otherweb-browsing enabled device.

A SELL node functions as a content creation station, a content server,and a program server. A SELL node implements the full range of mediaprocessing engine 11 digital content creation, visualization, anddissemination capabilities. In the current client-server embodiment,SELL nodes distribute (serve) merchant-tailored processing engine 11program components S-PROG to SHOP nodes, enabling SHOP nodes withprocessing engine 11 programs C-PROG. Alternately, SHOP nodes importtheir application software C-PROG from other mediums, such as CDROM.

In a collaborative content-creation workgroup configuration over network10, SELL nodes share their content and project databases 20, 21, 22, and27 with other SELL nodes.

SELL nodes create, package, and distribute 2D and 3D digitalrepresentations and ancillary information of the products and servicesassociated with the purveyor. Such media includes all combinations of 2Dproduct images, 3D product models, form and function parametric data,and other ancillary product information. Ancillary information includesmedia typically associated with product catalogs and brochures as wellas video and audio presentations. Three principal media packages createdand served from SELL nodes to client SHOP nodes are Intelligent ImagePackages IIP, Intelligent Object Packages IOP, and Intelligent AncillaryData packages IDP. Both SELL and SHOP nodes may also distribute allforms of data individually and not as a package.

Intelligent image packages IIP are 2D digital images or imagecompositions packaged and served with parametric data that enable a fullrange of 2D and 3D visualization and simulation capabilities at bothSELL and SHOP nodes. For example, a product purveyor offers the rugproduct of image I500 of FIG. 28 in several choices of patterns andsize. The purveyor SELL node can serve an intelligent image package IIPcontaining one or more images of the rug depicting color and patternoptions along with parametric shape and color option data.

Intelligent Object Packages IOP are packages of data associated with 3Dobject and scene constructions, including scene graphs, geometricstructures, texture maps, camera models, and parametric data controllingconstruction form and function. IOPs enable a full range of 3Dvisualization and simulation capabilities at both SELL and SHOP nodes.As an example, a SELL node offers cabinet product 90 depicted in imageI900 of FIG. 44. An intelligent object construction of cabinet 90 isshown in FIG. 38, with a single parametric drawer component 92 and covercomponent 91 modeled. As an intelligent object package, cabinet 90 isserved by the SELL node with its scene graph 300, imagery I900,geometric structures, camera models, shape parameters, and functionparameters (draw_trans_x, cover_rotate_z).

Intelligent Ancillary Data Packages IDP are portfolios of ancillaryinformation related to products and services distributed from SELLnodes. Ancillary information includes textual, audio, or videodescriptions of products and services. An IDP is packaged and servedwith IOPs and IIPs or separately.

IDP information is presented by the system through graphical displaymechanisms. The primary IDP displays are 2D image overlay “pop-up”mechanism PUP2 and 3-space embedded 3D graphical display pop-upmechanism PUP3. A PUP2 is a 2D image overlay similar to 2D graphicaloverlay and dialog mechanisms common to windows-based applications. APUP3 is a 3D a texture-mapped geometric “billboard” object that isembedded and rendered as an additional construction component of a 3Dscene. PUP2 and PUP3 mechanisms are attached to scene graph unit 14nodes of 2D image compositions and 3D object and scene constructions.SELL nodes generate, store, and disseminate PUP2 and PUP3 displays.

FIG. 30 shows collage composition image I401 with PUP2 graphic overlayPUP2-10 attached to the rug object. FIG. 29( b) shows correspondingcollage construction scene graph 250 with PUP2-10 attached to rug nodeN301. FIG. 47 shows an example PUP3 graphical pop-up display PUP3-30.The system allows the SELL node to create or import 3D geometry torepresent a PUP3. The system is not limited to any particular PUP3display geometry. For the present example PUP3-30, product informationis displayed on one face of the display geometry. FIG. 48 shows sceneimage I990 with 2D overlay display PUP2-20 attached to wooden CD cabinetproduct 90 and 3D pop-up display PUP3-30 attached to wooden storage boxproduct 91. FIG. 38( b) shows PUP3-30 attached to node N15 of scenegraph 300.

PUP2 and PUP3 mechanisms also serve as launch points for associated IDPmedia. Through user interactive input 804, the shopper launches videoand audio presentations about the product and the merchant by clickingon a PUP object or menus displayed on a PUP object.

PUP2 and PUP3 mechanisms also serve as portals for online productpurchasing and customer tracking transaction systems. When the PUPmechanism connects to a SELL node's purchasing and customer trackingsystems, PUP2 and PUP3 displays become the graphical user interface tothose systems.

Rendering engine 17 renders PUP displays in a scene graph when they areactivated. PUP displays may be re-positioned by the user orautomatically by the application program. As a 2D image overlay, a PUP2may move with the object it is attached to as a translation in the planeof the image. As a full 3D object, a PUP3 may move to any orientationwithin the 3-space coordinate system of the construction. In the presentembodiment, a PUP3 remains attached to a selected reference point of itsassociated 3D object and also rotates as a billboard to maintain aconsistent orientation relative to the current viewer position. Thisimplementation is analogous to dynamic 3D billboards implemented in 3Dgraphics visual simulation systems.

In the present embodiment, SHOP nodes download media processing engine11 “client-side” processing components from SELL server nodes. Some orall of media processing engine 11 functional components are downloadedover 10. The selection of components C-PROG downloaded depends on theapplication environment and is configurable by the system. SHOP nodesmay also import C-PROG processing components from other digital datamediums such as CDROM.

SHOP nodes create and integrate 2D and 3D digital environment andproduct representations and information. Central to their role in amerchandising environment is enabling a shopper to acquire andreconstruct their application environment and readily integrate andsimulate product constructions from SELL nodes. In addition, a SHOP nodemay also generate product 3D constructions directly from 2D imagery.When such information is either not available from a product purveyor oris not applicable to the circumstances. A shopper can acquire documentedand undocumented digital imagery from sources such as the Internet,digital cameras, and scanners, and transform them into 3Drepresentations. This extremely flexible program and processingdistribution model allows processing and data distribution to beefficiently tailored by the requirements of the commerce environment.

SHOP nodes download product-specific 2D digital images 21, scene graphsand 3D texture-mapped models 20, and project databases and ancillarydigital data 22 from SELL nodes or optionally from other SHOP nodes overnetwork 10. Media downloaded as individual components or as IOP, IIP,and IDP packages.

SHOP nodes import user 2D digital images 24 and 3D models 23 fromdirectly from digital acquisition devices, such as digital cameras andscanners. SHOP nodes may also download user-specific digital imagery 24and 3D models 23 from network 10 or other medium such as CDROM. Forexample, a user might photograph a scene or product with an analogcamera and employ a processing service that develops and digitizes filmand delivers the digital images to the user via the Internet or CDROM.

SHOP nodes employ the disclosed media integration methods to integrateuser generated constructions with downloaded or imported SELL nodeconstructions. SHOP nodes employ the simulation methods enabling SHOPnodes to simulate form and function of SELL node products within theirenvironments. In addition to spatial positioning, simulation includesexploration of product size, fit, color, and texture options as well asproduct functionality (e.g. open-close doors, turn on-off lights, testswitches).

Several usage scenarios for the disclosed merchandising system arepresented.

EXAMPLE 1

A home furnishings merchant displays items for sale at its e-commerceweb site on Internet 10. The site is enabled with the merchandisingsystem as a SELL server node SN200. Displayed at the merchant site isthe rug item depicted as image I500 of FIG. 28.

An online Internet shopper visits the merchant SELL site with interestin purchasing the rug. From the merchant site the shopper selects anoption to visualize the rug in her home. This selection launches thedownload of a merchant-tailored client-side version of media processingengine 11 software S-PROG from the merchant SELL site to the shoppercomputer system on network 10. This transaction enables the shopper as aSHOP node with media processing engine 11 software components C-PROG.

The shopper seeks to visualize rug I500 in her living room depicted inFIG. 18( a) image I200. The shopper acquires image I200 with a digitalcamera and downloads it as user data 24. For a wider field-of-view ofthe living room scene, the shopper takes multiple snapshots anddownloads them as user data 24. In the latter case, the shopper employsthe image mosaic processing features of media processing engine 11 tostitch the images together.

The shopper selects rug image I500 from the SELL site, triggering adownload of merchant product media from the SELL computer to the SHOPcomputer. The merchant SELL node transfers an intelligent image packageIIP for the rug product that includes three product images 21, one foreach rug color scheme offered, six size parameters denoting thedimensions of three rug sizes offered, and PC graphic PG6. The SELL nodealso transfers an Intelligent Ancillary Data Package IDP containingPUP2-10. No 3D object geometry 20 is transferred from SELL site to SHOP;rug geometry is generated by the SHOP node.

The shopper also takes her digital camera to a local retail store andacquires artwork image I502 of FIG. 28 to visualize it in the sameliving room with the rug. She imports image I502 from the digital camerainto her computer as user data 24. As a SHOP node, throughuser-interactive input 804, the shopper employs media processing engine11 configured in image collage construction mode to produce the collageof scene I200 with rug image I500 and artwork image I502, resulting inimage I401, shown in FIG. 30.

To view the rug options downloaded with the IIP, the SHOP nodeapplication allows the shopper to click on collage product items toscroll through the insertion of the three pattern options. The SHOP nodealso scales the collage geometry according to the merchant supplied sizeparameters, allowing the user to select and visualize the various rugsize offerings. When selected, PUP2-20 is displayed to provide onlineproduct information and launch the SELL node purchasing transactionsystem.

EXAMPLE 2

The shopper of Example 1 wants to interactively simulate placement ofthe rug in various positions on the floor within the living room scene.The shopper employs the system 3D scene construction mode.

The previously described process flow of FIG. 39, with the integrationof “rug” object of FIG. 28 image I500 within scene I200 of FIG. 18( a)and “true-to-scale” object insertion of FIG. 40, is executed. In herliving room, the shopper measures the distance between the back wall ofthe room and the marble fireplace base as well as the combined width ofthe two right French doors. The shopper aligns PC graphic PG1 as shownin FIG. 18( c) image I200 and enters the measured values for the widthand length parameters of PC structure PS20. The scene constructionbecomes calibrated to the true dimensions of the shopper's living room.

To place the rug on the floor of the room, the shopper selects point VF5on the rug in image I500 and anchor point 933 on the floor in image. Thesystem automatically places and dimensions the rug on the floor with thetrue rug dimensions downloaded with the merchant IIP. Using the IIPinformation and the system interactive simulation functions, the shopperinteractively repositions the rug to various locations on the floor andscrolls through the various rug sizes, patterns, and colors. The systemautomatically and seamlessly interchanges IIP supplied shape parameters,texture maps, and color data.

EXAMPLE 3

-   -   A shopper of home furnishings visits a merchant e-commerce web        site on network 10. The merchant-site is enabled as a SELL        server node SN200. The shopper selects an option at the merchant        site to visualize products in his home. This selection launches        the download of a merchant-tailored media processing engine 11        software S-PROG from the merchant SELL site to the shoppers'        computer system on network 10. This transaction enables the        shopper as a SHOP node CN100 with media processing engine 11        software C-PROG.

Displayed at the merchant site is pine storage box 91 depicted in FIG.44 image I901. The shopper selects image I901 and the SELL nodetransfers an IOP and an IDP for the product to the shopper SHOP node.The IOP transferred contains the product scene graph, geometricstructures, camera models, texture map imagery, and parametric shape andfunction parameters. The IDP contains graphic information displayPUP3-30.

The shopper visits web site of another merchant on network 10. Thesecond merchant site is also enabled as a SELL server node SN200.Displayed at the merchant site is CD cabinet 90 depicted in FIG. 44image I900. The shopper selects image I900 and the merchant SELL nodetransfers an IOP and an IDP, including PUP2-20, for the product.

The shopper visits a third web site on network 10. At that e-commercesite, the merchant displays wood cabinet 93 as image I903 of FIG. 45.This merchant site is not enabled as an SELL node, so the shopperdownloads only image I903 using the image download facility of his webbrowser. The shopper manually records the advertised dimensions of theproduct for future manual entry into the SHOP node system.

The shopper takes a picture of the room he has in mind for the products.The shopper acquires scene image I700 of with a conventional camera andhas the film developed through a service that provides digital imagesreturned over the Internet. The shopper downloads his image I700 as userdata 24 over network 10 from the film service.

The shopper visits a local retail store and acquires photographs of themirror product of image I904 of FIG. 45 and the artwork product of imageI502 FIG. 28. No dimensions for these items are obtained. Thesephotographs are processed into digital images and downloaded into theSHOP node in the same manner as scene image I770.

Employing the C-PROG 3D scene construction program downloaded from thefirst merchant SELL node, the shopper produces the 3D construction fromscene image I700. The shopper supplies actual room dimensions thatcorrespond to the dimensions S1 and S2 of PC structure P510. The roomscene geometry and camera model are calibrated to true scale. If agreater extent of the room scene is desired, the shopper would acquiremore images of the room and continue with the 3D scene constructionprocess in multi-image mode.

Employing C-PROG, 3D object construction program downloaded from thefirst merchant SELL node, the shopper constructs a parametric 3Dtexture-mapped model for brown cabinet 93. The parametric model isproduced true scale using the product dimensions recorded by theshopper.

The shopper then uses the construction “true-to-scale” object insertionprocess to insert SELL node supplied 3D models for pine storage box 91and CD cabinet 90 and shopper constructed 3D model for brown cabinet 93onto the floor of the shopper constructed 3D scene. The “force-fit”insertion process is employed to place mirror 94 and the artwork ofimage I502 on the walls of the 3D scene construction. This entails theuser placing a PC graphic in each product image as the product sourceregions and a corresponding PC graphic on the wall locations desired asthe destination regions. For the placement of mirror product 94, thesystem also employs the rendering engine 17 projective alpha channelmasking and composition layering to produce the detail of the wroughtiron frame.

The selected products are then simulated in 3D in the shopper's roomscene, with the user interactively navigating the scene, repositioningthe products, and exploring the functional IOP components. FIG. 46images I910, I920, I930, and I940 show several frames of a real-timeinteractive 3D simulation rendered by the SHOP node. The two cabinetsand the pine box are placed at various positions on the floor throughoutthe 3D room scene. The mirror and artwork products remain attached inplace on the walls. In image I910, the mirror is moved on the wall toanother location (not visible).

FIG. 48 (image I990) is a frame of the output display showing thedeployment of PUP2-20 attached to the CD cabinet and PUP3-30 attached tothe pine storage box. In the present example, the shopper clicks on thePUPs to view and hear about the product specifications, pricing, andoptions. To order a product, the user clicks on a PUP and the systemlaunches the order transaction system of the attached SELL node.

1. A computer implemented method of determining camera parameters in athree-dimensional space from a two-dimensional image of objects in thethree-dimensional space, said computer comprising one or more processor,memory, one or more user input device, one or more data output device,and one or more data storage device, wherein data relating to saidtwo-dimensional image of objects in the three dimensional space istransformed into a calculation of said camera's parameters, the methodcomprising the steps of: identifying a polygon, in perspective, in theimage that corresponds to a planar facet in the space; and calculatingcamera parameters including a camera position from the polygon; whereinthe step of calculating comprises: identifying parameterized geometricprimitives in the space; determining the elements of the primitives,wherein an element is a spatial structure projectable onto the image;mapping features of the image to the elements to form correspondences;and solving for an objective function that minimizes an aggregate errorbetween features of the image and projections of elements onto theimage; wherein said steps are performed on said computer.
 2. Thecomputer implemented method according to claim 1, further comprising:prior to calculating the camera parameters, identifying at least onedimension of an edge of the planar facet; and calculating the cameraparameters including the camera position from the polygon and thedimension.
 3. The computer implemented method according to claim 1,wherein the camera parameters further include a focal length.
 4. Thecomputer implemented method according to claim 3 further comprising:prior to calculating the camera parameters, identifying at least onedimension of an edge of the planar facet; and calculating the cameraparameters including the camera position from the polygon and thedimension.
 5. A computer implemented method of interactively determiningcamera parameters of a camera in a three-dimensional space from atwo-dimensional image of objects in the three-dimensional space, saidcomputer comprising one or more processor, memory, one or more userinput device, one or more data output device, and one or more datastorage device, wherein data relating to said two-dimensional image ofobjects in the three dimensional space is transformed into a calculationof said camera's parameters, the method comprising the steps of:interactively modifying a cursor graphic, superimposed on the image suchthat it appears to lie in perspective on a planar facet in the space;and calculating camera parameters including a camera position the cursorgraphic, wherein the step of calculating comprises: identifyingparameterized geometric primitives in the space; determining theelements of the primitives, wherein an element is a spatial structureprojectable onto the image; mapping features of the cursor graphic tothe elements to form correspondences; and solving for an objectivefunction that minimizes an aggregate error between features of thecursor graphic and projections of elements onto the image; wherein saidsteps are performed on said computer.
 6. The computer implemented methodaccording to claim 5, wherein the cursor graphic is an n-sided polygon.7. The computer implemented method according to claim 5, wherein thecamera parameters further includes a focal length.
 8. The computerimplemented method according to claim 5, further comprising: prior tocalculating the camera parameters, identifying at least one dimension ofan edge of the planar facet; and calculating the camera parametersincluding the camera position from the cursor graphic and the dimension.9. The computer implemented method according to claim 7, furthercomprising: prior to calculating the camera parameters, identifying atleast one dimension of an edge of the planar facet; and calculating thecamera parameters including the camera position from the cursor graphicand the dimension.
 10. The computer implemented method according toclaim 1, wherein calculating the camera parameters does not includeprior knowledge of a focal length of a camera.
 11. The computerimplemented method according to claim 10, further comprising: prior tocalculating the camera parameters, identifying at least one dimension ofan edge of the planar facet; and calculating the camera parametersincluding the camera position from the cursor graphic and the dimension.12. A computer system for determining camera parameters in athree-dimensional space from a two-dimensional image of objects in thethree-dimensional space, said computer system comprising: one or moreprocessor for performing instructions for: identifying a polygon, inperspective, in the image that corresponds to a planar facet in thespace; and calculating camera parameters including a camera positionfrom the polygon; wherein the instruction for calculating cameraparameters comprises: identifying parameterized geometric primitives inthe space; determining the elements of the primitives, wherein anelement is a spatial structure projectable onto the image; mappingfeatures of the image to the elements to form correspondences; andsolving for an objective function that minimizes an aggregate errorbetween features of the image and projections of elements onto theimage; one or more data storage device for storing said instructions.13. The computer system according to claim 12, further comprising:instructions for: prior to calculating the camera parameters,identifying at least one dimension of an edge of the planar facet; andcalculating the camera parameters including the camera position from thepolygon and the dimension.
 14. The computer system according to claim12, wherein the camera parameters calculations further includeinstructions for calculating a focal length.
 15. The computer systemaccording to claim 14, further comprising: instructions for: identifyingat least one dimension of an edge of the planar facet to be executedprior to calculating the camera parameters; and calculating the cameraparameters including the camera position from the polygon and thedimension.
 16. The computer system according to claim 12, whereininstructions for calculating the camera parameters do not include priorknowledge of focal length of the camera.
 17. The computer systemaccording to claim 16, further comprising: instructions for: prior tocalculating the camera parameters, identifying at least one dimension ofan edge of the planar facet; and calculating the camera parametersincluding the camera position from the cursor graphic and the dimension.18. A computer system for interactively determining camera parameters ina three-dimensional space from a two-dimensional image of objects in thethree-dimensional space, said computer system comprising: one or moreprocessor for performing instructions for: interactively modifying acursor graphic superimposed on the image such that it appears to lie inperspective on a planar facet in the space; and calculating cameraparameters including a camera position from the cursor graphic, whereinthe instruction for calculating comprises: identifying parameterizedgeometric primitives in the space; determining the elements of theprimitives, wherein an element is a spatial structure projectable ontothe image; mapping features of the cursor graphic to the elements toform correspondences; and solving for an objective function thatminimizes an aggregate error between features of the cursor graphic andprojections of elements onto the image; one or more data storage devicefor storing said instructions.
 19. The computer system according toclaim 5, wherein the cursor graphic is an n-sided polygon.
 20. Thecomputer system according to claim 18, wherein the camera parameterscalculations further includes instructions for calculating a focallength.
 21. The computer system according to claim 18, furthercomprising: instructions for: prior to calculating the cameraparameters, identifying at least one dimension of an edge of the planarfacet; and calculating the camera parameters including the cameraposition from the cursor graphic and the dimension.
 22. A computerimplemented method of interactively determining camera parameters of acamera in a three-dimensional space from a two-dimensional image ofobjects in the three-dimensional image of space, said computercomprising one or more processor, memory, one or more user input device,one or more data output device, and one or more data storage devices,wherein data relating to said two-dimensional image of objects in thethree dimensional space is transformed into a calculation of saidcamera's parameters, the method comprising the steps of: interactivelymodifying a cursor graphic, superimposed on the image such that itappears to lie in perspective relative to at least one planar facet inthe space; and calculating camera parameters including a camera positionfrom the cursor graphic; wherein the cursor graphic corresponds to ageometric structure that does not exist in the physical space or isfully obscured in the two-dimensional image.
 23. The method according toclaim 22, wherein the cursor graphic is a rectangle.
 24. The methodaccording to claim 22, wherein the cursor graphic is an n-sided polygon.25. The method according to claim 22, wherein the cursor graphic is acube.
 26. A computer system for interactively determining cameraparameters in a three-dimensional space from a two-dimensional image ofthe three-dimensional space, said computer system comprising: one ormore processors for performing instructions for: interactively modifyinga cursor graphic superimposed on the image, said cursor graphicsrepresenting a geometric structure that does not exist in the physicalspace or is fully obscured in the two-dimensional image, such that thecursor graphic appears to lie in perspective relative to at least oneplanar facet in the space; and calculating camera parameters including acamera position from the cursor graphic, one or more data storage devicefor storing said instructions.
 27. The computer system according toclaim 26, wherein the cursor graphic is a rectangle.
 28. The computersystem according to claim 26, wherein the cursor graphic is an n-sidedpolygon.
 29. The computer system according to claim 26, wherein thecursor graphic is a cube.
 30. The computer system according to claim 26,further comprising: instructions for: prior to calculating the cameraparameters, identifying at least one dimension of an edge of the cursorgraphic; and calculating the camera parameters including the cameraposition from the cursor graphic and the dimension.
 31. The computersystem according to claim 26, wherein the camera parameters calculationsfurther include instructions for calculating a focal length.
 32. Thecomputer system according to claim 31 further comprising: instructionsfor: identifying at least one dimension of an edge of the cursor graphicto be executed prior to calculating the camera parameters; andcalculating the camera parameters including the camera position from thecursor graphic and the dimension.
 33. A computer system forinteractively determining camera parameters in a three-dimensional spacefrom a two-dimensional image of the three-dimensional space, saidcomputer system comprising: a display device comprising, a means forreceiving at least one image of the space; a means for displaying the atleast one image of the space on the display; and a means for user input,operable to interactively modify cursor graphic superimposed on the atleast one image of the space on the display; a first memory for storingthe at least one image of the space; one or more processors forperforming instructions for: interactively modifying a cursor graphicsuperimposed on the image; said cursor graphic representing a geometricstructure that does not exist in the space or is fully obscured in thetwo-dimensional image such that the cursor graphic appears to lie inperspective relative to at least one planar facet in the space; andcalculating camera parameters including a camera position from thecursor graphic; a second memory for storing said instructions; a thirdmemory for storing said camera parameters; wherein said first, secondand third memory are implementable in one or more data repository. 34.The computer system according to claim 33, configured to function asclient-server system wherein the display device is a client connectedover a network to a server, and the calculating of camera parameters isperformed by the client.
 35. The computer system according to claim 33,configured to function as client-server system wherein the displaydevice is a client connected over a network to a server, and thecalculating of camera parameters is performed by the server.